A ResNet-101 deep learning framework induced transfer learning strategy for moving object detection
Background subtraction is a crucial stage in many visual surveillance systems. The prime objective of any such system is to detect moving objects such that the system could be utilized to face many real-time challenges. In the last few decades, various methods have been developed to detect moving ob...
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| Veröffentlicht in: | Image and vision computing Jg. 146; S. 105021 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.06.2024
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| ISSN: | 0262-8856, 1872-8138 |
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| Abstract | Background subtraction is a crucial stage in many visual surveillance systems. The prime objective of any such system is to detect moving objects such that the system could be utilized to face many real-time challenges. In the last few decades, various methods have been developed to detect moving objects. However, the performance of many existing methods needs further improvement for slow, moderate, and fast-moving object detection in videos simultaneously and also for unseen video setups. In this article, a noteworthy effort is made to detect moving objects in complex videos by harnessing the potential of an encoder-decoder-type deep framework, employing a customized ResNet-101 model along with a feature pooling framework (FPF). The proposed algorithm has four-fold innovations including: A pre-trained modified ResNet-101 network with a transfer learning technique is proposed as an encoder to learn the challenging video scene adequately. The proposed encoder network employs a total of twenty three numbers of layers with skip connections making the model less complex. In between the encoder and decoder framework, the FPF module is used that combines a max-pooling layer, a convolutional layer, and multiple convolutional layers with varying sampling rates. This FPM module can preserve multi-scale and multi-dimensional features across different levels accurately. A decoder architecture consisting of stacked convolution layers is implemented to transform the features into image space efficiently. The efficiency of the proposed scheme is corroborated using subjective and objective analysis. The efficiency of the developed model is highlighted through a comparison with thirty-three existing methods, effectively illustrating its superior efficacy.
•A ResNet-101 encoder-decoder with feature pooling is developed for moving object detection.•Transfer learning updates the modified ResNet-101 weights on challenging video datasets.•The modified ResNet-101 network with 23 layers is computationally efficient.•The technique detects objects of varying speeds with high accuracy on challenging datasets. |
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| AbstractList | Background subtraction is a crucial stage in many visual surveillance systems. The prime objective of any such system is to detect moving objects such that the system could be utilized to face many real-time challenges. In the last few decades, various methods have been developed to detect moving objects. However, the performance of many existing methods needs further improvement for slow, moderate, and fast-moving object detection in videos simultaneously and also for unseen video setups. In this article, a noteworthy effort is made to detect moving objects in complex videos by harnessing the potential of an encoder-decoder-type deep framework, employing a customized ResNet-101 model along with a feature pooling framework (FPF). The proposed algorithm has four-fold innovations including: A pre-trained modified ResNet-101 network with a transfer learning technique is proposed as an encoder to learn the challenging video scene adequately. The proposed encoder network employs a total of twenty three numbers of layers with skip connections making the model less complex. In between the encoder and decoder framework, the FPF module is used that combines a max-pooling layer, a convolutional layer, and multiple convolutional layers with varying sampling rates. This FPM module can preserve multi-scale and multi-dimensional features across different levels accurately. A decoder architecture consisting of stacked convolution layers is implemented to transform the features into image space efficiently. The efficiency of the proposed scheme is corroborated using subjective and objective analysis. The efficiency of the developed model is highlighted through a comparison with thirty-three existing methods, effectively illustrating its superior efficacy.
•A ResNet-101 encoder-decoder with feature pooling is developed for moving object detection.•Transfer learning updates the modified ResNet-101 weights on challenging video datasets.•The modified ResNet-101 network with 23 layers is computationally efficient.•The technique detects objects of varying speeds with high accuracy on challenging datasets. |
| ArticleNumber | 105021 |
| Author | Panda, Ganapati Panigrahi, Upasana Panda, Manoj Kumar Sahoo, Prabodh Kumar |
| Author_xml | – sequence: 1 givenname: Upasana surname: Panigrahi fullname: Panigrahi, Upasana organization: Department of Electronics and Communication Engineering, C V Raman Global University, Mahura, Janla, Bhubaneswar 752054, Odisha, India – sequence: 2 givenname: Prabodh Kumar surname: Sahoo fullname: Sahoo, Prabodh Kumar email: sahooprabodhkumar@gmail.com organization: Department of Mechatronics Engineering, Parul Institute of Technology, Parul University, Waghodia, Vadodara 391760, Gujarat, India – sequence: 3 givenname: Manoj Kumar surname: Panda fullname: Panda, Manoj Kumar email: manojkumarpanda@giet.edu organization: Department of Electronics and Communication Engineering, GIET University, Gunupur, Rayagada 765022, Odisha, India – sequence: 4 givenname: Ganapati surname: Panda fullname: Panda, Ganapati organization: Department of Electronics and Communication Engineering, C V Raman Global University, Mahura, Janla, Bhubaneswar 752054, Odisha, India |
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| Cites_doi | 10.1007/s11760-017-1093-8 10.1016/j.cosrev.2014.04.001 10.1016/j.cviu.2022.103501 10.1016/j.ijleo.2018.04.047 10.1016/j.neunet.2019.04.024 10.1016/j.imavis.2006.01.001 10.1117/1.JEI.27.2.023002 10.1109/TPAMI.2003.1233909 10.1109/TIP.2017.2695882 10.1109/34.120329 10.1109/ACCESS.2018.2861223 10.1007/s11760-016-0975-5 10.1016/j.patrec.2016.09.014 10.1109/ACCESS.2018.2812880 10.1016/j.aasri.2012.06.077 10.1016/j.imavis.2009.11.014 10.3390/brainsci13040555 10.1016/j.eswa.2017.12.009 10.1109/TIP.2010.2101613 10.1109/TSMCC.2004.829274 10.1016/j.aeue.2011.07.009 10.1049/iet-cvi.2018.5642 10.1109/TCSVT.2021.3088130 10.1016/j.neucom.2019.04.088 10.1016/j.neucom.2016.12.038 10.1142/S0129065717500563 10.1109/ACCESS.2016.2608847 10.1109/TMI.2016.2528162 10.1007/s11760-018-1278-9 10.1109/ACCESS.2021.3071163 10.1109/ACCESS.2019.2914961 10.1016/j.patcog.2017.09.040 10.1109/TITS.2006.874722 10.1109/TCSVT.2020.3014663 10.1109/TEVC.2017.2694160 10.1109/TCSVT.2017.2711659 10.1007/s11042-019-7411-0 10.1016/j.eswa.2020.114544 10.3390/sym11050621 10.1109/TIT.2020.2983698 10.1109/TPAMI.2011.155 10.1109/TITS.2020.3030801 10.1007/s11760-014-0747-z |
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| Keywords | Deep learning architecture Feature pooling framework Background subtraction Transfer learning Contrast normalization |
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| References | Sengar, Mukhopadhyay (bb0155) 2017; 11 He, Zhang, Ren, Sun (bb0240) 2016 Rout, Subudhi, Veerakumar, Chaudhury (bb0030) 2018; 97 Kalsotra, Arora (bb0040) 2019; 7 Dollar, Wojek, Schiele, Perona (bb0110) 2012; 34 Zheng, Wang, Wang (bb0260) 2020; 394 Viola, Jones (bb0105) 2001; vol. 1 Ren, He, Girshick, Sun (bb0180) 2016 Panda, Subudhi, Veerakumar, Jakhetiya (bb0005) 2023 Huang, Zou, Zheng, Zhu (bb0140) 2019 Fan, Zhang, Wenli (bb0145) 2021; 170 Braham, Piérard, Van Droogenbroeck (bb0290) 2017 Sahoo, Kanungo, Parvathi (bb0090) 2014 Yang, Ruan, Zhang, Cheng, Zhang, Xie (bb0360) 2021; 32 Xu, Ye, Li, Liu, Yang, Ding (bb0375) 2014 Paolo Spagnolo, Leo (bb0230) 2006; 24 Zhang, Xiao, Huang, Zhang, Han (bb0170) 2021; 31 St-Charles, Bilodeau, Bergevin (bb0315) 2015 Mandal, Vipparthi (bb0010) 2020; 23 Christopher Montgomery (bb0085) 2004 Lee, Lee, Yoo, Kwon (bb0270) 2019; 11 Fisher, Chen-Burger, Giordano, Hardman, Lin (bb0355) 2016; vol. 104 Zhihang, Turki, Phan, Wang (bb0035) 2018; 6 Tezcan, Ishwar, Konrad (bb0255) 2021; 9 De Gregorio, Giordano (bb0320) 2017 Subudhi, Nanda (bb0245) 2008 Abdullahi, Bature, Gabralla, Chiroma (bb0235) 2023; 13 Zhu, Wang (bb0250) 2012; 66 Panda, Subudhi, Bouwmans, Jakheytiya, Veerakumar (bb0285) 2022 Xia, Song, He (bb0210) 2016; 10 Fatih Savaş, Demirel, Erkal (bb0165) 2018; 168 Sajid, Cheung (bb0325) 2017; 26 KaewTraKulPong, Bowden (bb0135) 2002 Işık, Özkan, Günal, Gerek (bb0305) 2018; 27 Wang, Jodoin, Porikli, Konrad, Benezeth, Ishwar (bb0075) 2014 Zhang, Liu, Lian, Wang (bb0225) 2010 Kebir, Taibi (bb0390) 2022; 12 Liu, Anguelov, Erhan, Szegedy, Reed, Cheng-ang, Berg (bb0175) 2016 Allebosch, Van Hamme, Deboeverie, Veelaert, Philips (bb0370) 2016 Sahoo, Kanungo, Mishra (bb0120) 2018; 12 Barnich, Van Droogenbroeck (bb0205) 2011; 20 Gracewell, John (bb0385) 2020; 79 Weiming, Tan, Wang, Maybank (bb0025) 2004; 34 Huang, Wu, Huang (bb0160) 2012; 1 Bouwmans (bb0050) 2014; 11 Kanungo, Narayan, Sahoo, Mishra (bb0130) 2017 Bianco, Ciocca, Schettini (bb0295) 2017; 21 Subudhi, Panda, Veerakumar, Jakhetiya, Esakkirajan (bb0045) 2022 Poppe (bb0015) 2010; 28 Mondéjar-Guerra, Rouco, Novo, Ortega (bb0300) 2019 López-Rubio, Molina-Cabello, Luque-Baena, Domínguez (bb0350) 2018; 28 Shin, Roth, Gao, Le, Ziyue, Nogues, Yao, Mollura, Summers (bb0070) 2016; 35 Duncan, Chou (bb0095) 1992; 14 An, Xu, Yu, Guo, Zhao, Tang, Wang (bb0365) 2023 Lin, Goyal, Girshick, He, Dollar (bb0185) Oct 2017 Zhu, Jiao, Tse (bb0200) 2020; 66 Wang, Luo, Jodoin (bb0280) 2017; 96 Law, Deng (bb0195) 2018 Guo, Wang, Bai, Zhang, Li (bb0150) sep 2017; 242 Cioppa, Van Droogenbroeck, Braham (bb0340) 2020 Hsieh, Shih, Chen, Wen (bb0020) 2006; 7 Jiang, Xiaobo (bb0335) 2017; 28 Zhou, Wang, Krähenbühl (bb0190) 2019 Redmon, Divvala, Girshick, Farhadi (bb0115) 2016 Panda, Sharma, Bajpai, Subudhi, Thangaraj, Jakhetiya (bb0055) 2022; 222 Reisslein, Karam, Seeling, Fitzek (bb0080) 2000 Choudhury, Sa, Bakshi, Majhi (bb0100) 2016; 4 Tezcan, Ishwar, Konrad (bb0265) 2020 Dou, Qin, Zimei (bb0215) 2017; 11 Wang, Gou, Wang (bb0345) 2018; 6 Toyama, Krumm, Brumitt, Meyers (bb0380) 1999; vol. 1 Cucchiara, Grana, Piccardi, Prati (bb0125) 2003; 25 Babaee, Dinh, Rigoll (bb0275) 2018; 76 Işık, Özkan, Gerek (bb0310) 2019; 13 Sahoo, Kanungo, Mishra, Mohanty (bb0220) 2022; 34 Martins, Carvalho, Corte-Real, Alba-Castro (bb0330) 2017 Liu, Wang, Liu, Zeng, Liu, Alsaadi (bb0060) 2017; 234 Bouwmans, Javed, Sultana, Jung (bb0065) 2019; 117 Sauvalle, de La Fortelle (bb0395) 2023 Mondéjar-Guerra (10.1016/j.imavis.2024.105021_bb0300) 2019 Jiang (10.1016/j.imavis.2024.105021_bb0335) 2017; 28 Rout (10.1016/j.imavis.2024.105021_bb0030) 2018; 97 Panda (10.1016/j.imavis.2024.105021_bb0055) 2022; 222 Shin (10.1016/j.imavis.2024.105021_bb0070) 2016; 35 Liu (10.1016/j.imavis.2024.105021_bb0175) 2016 Hsieh (10.1016/j.imavis.2024.105021_bb0020) 2006; 7 Paolo Spagnolo (10.1016/j.imavis.2024.105021_bb0230) 2006; 24 Guo (10.1016/j.imavis.2024.105021_bb0150) 2017; 242 Işık (10.1016/j.imavis.2024.105021_bb0305) 2018; 27 Liu (10.1016/j.imavis.2024.105021_bb0060) 2017; 234 Panda (10.1016/j.imavis.2024.105021_bb0285) 2022 Duncan (10.1016/j.imavis.2024.105021_bb0095) 1992; 14 Panda (10.1016/j.imavis.2024.105021_bb0005) 2023 Subudhi (10.1016/j.imavis.2024.105021_bb0245) 2008 Zhang (10.1016/j.imavis.2024.105021_bb0170) 2021; 31 Barnich (10.1016/j.imavis.2024.105021_bb0205) 2011; 20 Toyama (10.1016/j.imavis.2024.105021_bb0380) 1999; vol. 1 Zhu (10.1016/j.imavis.2024.105021_bb0250) 2012; 66 López-Rubio (10.1016/j.imavis.2024.105021_bb0350) 2018; 28 Mandal (10.1016/j.imavis.2024.105021_bb0010) 2020; 23 Ren (10.1016/j.imavis.2024.105021_bb0180) 2016 Poppe (10.1016/j.imavis.2024.105021_bb0015) 2010; 28 Lin (10.1016/j.imavis.2024.105021_bb0185) 2017 Babaee (10.1016/j.imavis.2024.105021_bb0275) 2018; 76 Kalsotra (10.1016/j.imavis.2024.105021_bb0040) 2019; 7 Choudhury (10.1016/j.imavis.2024.105021_bb0100) 2016; 4 Xu (10.1016/j.imavis.2024.105021_bb0375) 2014 Xia (10.1016/j.imavis.2024.105021_bb0210) 2016; 10 Wang (10.1016/j.imavis.2024.105021_bb0345) 2018; 6 Sajid (10.1016/j.imavis.2024.105021_bb0325) 2017; 26 Tezcan (10.1016/j.imavis.2024.105021_bb0255) 2021; 9 Martins (10.1016/j.imavis.2024.105021_bb0330) 2017 Kebir (10.1016/j.imavis.2024.105021_bb0390) 2022; 12 Law (10.1016/j.imavis.2024.105021_bb0195) 2018 Fan (10.1016/j.imavis.2024.105021_bb0145) 2021; 170 Zhang (10.1016/j.imavis.2024.105021_bb0225) 2010 Redmon (10.1016/j.imavis.2024.105021_bb0115) 2016 Gracewell (10.1016/j.imavis.2024.105021_bb0385) 2020; 79 Huang (10.1016/j.imavis.2024.105021_bb0140) 2019 Abdullahi (10.1016/j.imavis.2024.105021_bb0235) 2023; 13 Tezcan (10.1016/j.imavis.2024.105021_bb0265) 2020 Zheng (10.1016/j.imavis.2024.105021_bb0260) 2020; 394 Wang (10.1016/j.imavis.2024.105021_bb0280) 2017; 96 St-Charles (10.1016/j.imavis.2024.105021_bb0315) 2015 Işık (10.1016/j.imavis.2024.105021_bb0310) 2019; 13 He (10.1016/j.imavis.2024.105021_bb0240) 2016 An (10.1016/j.imavis.2024.105021_bb0365) 2023 Sahoo (10.1016/j.imavis.2024.105021_bb0120) 2018; 12 Fatih Savaş (10.1016/j.imavis.2024.105021_bb0165) 2018; 168 Zhihang (10.1016/j.imavis.2024.105021_bb0035) 2018; 6 Dollar (10.1016/j.imavis.2024.105021_bb0110) 2012; 34 Dou (10.1016/j.imavis.2024.105021_bb0215) 2017; 11 Wang (10.1016/j.imavis.2024.105021_bb0075) 2014 Subudhi (10.1016/j.imavis.2024.105021_bb0045) 2022 Viola (10.1016/j.imavis.2024.105021_bb0105) 2001; vol. 1 Kanungo (10.1016/j.imavis.2024.105021_bb0130) 2017 KaewTraKulPong (10.1016/j.imavis.2024.105021_bb0135) 2002 De Gregorio (10.1016/j.imavis.2024.105021_bb0320) 2017 Huang (10.1016/j.imavis.2024.105021_bb0160) 2012; 1 Lee (10.1016/j.imavis.2024.105021_bb0270) 2019; 11 Fisher (10.1016/j.imavis.2024.105021_bb0355) 2016; vol. 104 Weiming (10.1016/j.imavis.2024.105021_bb0025) 2004; 34 Bouwmans (10.1016/j.imavis.2024.105021_bb0065) 2019; 117 Bouwmans (10.1016/j.imavis.2024.105021_bb0050) 2014; 11 Braham (10.1016/j.imavis.2024.105021_bb0290) 2017 Allebosch (10.1016/j.imavis.2024.105021_bb0370) 2016 Cucchiara (10.1016/j.imavis.2024.105021_bb0125) 2003; 25 Sahoo (10.1016/j.imavis.2024.105021_bb0220) 2022; 34 Sahoo (10.1016/j.imavis.2024.105021_bb0090) 2014 Zhu (10.1016/j.imavis.2024.105021_bb0200) 2020; 66 Yang (10.1016/j.imavis.2024.105021_bb0360) 2021; 32 Sengar (10.1016/j.imavis.2024.105021_bb0155) 2017; 11 Reisslein (10.1016/j.imavis.2024.105021_bb0080) 2000 Bianco (10.1016/j.imavis.2024.105021_bb0295) 2017; 21 Christopher Montgomery (10.1016/j.imavis.2024.105021_bb0085) 2004 Cioppa (10.1016/j.imavis.2024.105021_bb0340) 2020 Zhou (10.1016/j.imavis.2024.105021_bb0190) 2019 Sauvalle (10.1016/j.imavis.2024.105021_bb0395) 2023 |
| References_xml | – volume: 28 start-page: 1750056 year: 2018 ident: bb0350 article-title: Foreground detection by competitive learning for varying input distributions publication-title: Int. J. Neural Syst. – volume: 79 start-page: 4639 year: 2020 end-page: 4659 ident: bb0385 article-title: Dynamic background modeling using deep learning autoencoder network publication-title: Multimed. Tools Appl. – volume: 242 year: sep 2017 ident: bb0150 article-title: A new moving object detection method based on frame-difference and background subtraction publication-title: IOP Conf. Ser. Mater. Sci. Eng. – start-page: 433 year: 2016 end-page: 454 ident: bb0370 article-title: C-efic: Color and edge based foreground background segmentation with interior classification publication-title: Computer Vision, Imaging and Computer Graphics Theory and Applications: 10th International Joint Conference, VISIGRAPP 2015, Berlin, Germany, March 11–14, 2015, Revised Selected Papers 10 – start-page: 1 year: 2017 end-page: 6 ident: bb0130 article-title: Neighborhood based codebook model for moving object segmentation publication-title: Proceedings of the 2nd International Conference on Man and Machine Interfacing – year: Oct 2017 ident: bb0185 article-title: Focal loss for dense object detection publication-title: Proceedings of the IEEE International Conference on Computer Vision (ICCV) – start-page: 107 year: 2014 end-page: 116 ident: bb0375 article-title: Dynamic background learning through deep auto-encoder networks publication-title: Proceedings of the 22nd ACM international conference on Multimedia – volume: 21 start-page: 914 year: 2017 end-page: 928 ident: bb0295 article-title: Combination of video change detection algorithms by genetic programming publication-title: IEEE Trans. Evol. Comput. – volume: 11 start-page: 1 year: 2019 end-page: 15 ident: bb0270 article-title: WisenetMD: motion detection using dynamic background region analysis publication-title: Symmetry – volume: vol. 1 start-page: 255 year: 1999 end-page: 261 ident: bb0380 article-title: Wallflower: Principles and practice of background maintenance publication-title: Proceedings of the 7th IEEE International Conference on Computer Vision – start-page: 1 year: 2023 end-page: 14 ident: bb0005 article-title: Modified ResNet-152 network with hybrid pyramidal pooling for local change detection publication-title: IEEE Trans. Artif. Intell. – volume: 6 start-page: 43450 year: 2018 end-page: 43459 ident: bb0035 article-title: A 3D Atrous convolutional long short-term memory network for background subtraction publication-title: IEEE Access – volume: 66 start-page: 249 year: 2012 end-page: 254 ident: bb0250 article-title: A hybrid algorithm for automatic segmentation of slowly moving objects publication-title: AEU Int. J. Electron. Commun. – volume: 11 start-page: 31 year: 2014 end-page: 66 ident: bb0050 article-title: Traditional and recent approaches in background modeling for foreground detection: an overview publication-title: Comput. Sci. Rev. – volume: 13 start-page: 555 year: 2023 ident: bb0235 article-title: Lie recognition with multi-modal spatial–temporal state transition patterns based on hybrid convolutional neural network–bidirectional long short-term memory publication-title: Brain Sci. – volume: 9 start-page: 53849 year: 2021 end-page: 53860 ident: bb0255 article-title: Bsuv-net 2.0: spatio-temporal data augmentations for video-agnostic supervised background subtraction publication-title: IEEE Access – volume: 4 start-page: 6133 year: 2016 end-page: 6150 ident: bb0100 article-title: An evaluation of background subtraction for object detection vis-a-vis mitigating challenging scenarios publication-title: IEEE Access – volume: vol. 104 year: 2016 ident: bb0355 article-title: Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data – volume: 35 start-page: 1285 year: 2016 end-page: 1298 ident: bb0070 article-title: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning publication-title: IEEE Trans. Med. Imaging – start-page: 779 year: 2016 end-page: 788 ident: bb0115 article-title: You only look once: Unified, real-time object detection publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – volume: 31 start-page: 1804 year: 2021 end-page: 1818 ident: bb0170 article-title: Revisiting feature fusion for rgb-t salient object detection publication-title: IEEE Trans. Circuits Syst. Video Technol. – volume: 76 start-page: 635 year: 2018 end-page: 649 ident: bb0275 article-title: A deep convolutional neural network for video sequence background subtraction publication-title: Pattern Recogn. – volume: 23 start-page: 2031 year: 2020 end-page: 2044 ident: bb0010 article-title: Scene independency matters: an empirical study of scene dependent and scene independent evaluation for CNN-based change detection publication-title: IEEE Trans. Intell. Transp. Syst. – volume: 32 start-page: 2145 year: 2021 end-page: 2157 ident: bb0360 article-title: Stpnet: a spatial-temporal propagation network for background subtraction publication-title: IEEE Trans. Circuits Syst. Video Technol. – volume: 28 start-page: 976 year: 2010 end-page: 990 ident: bb0015 article-title: A survey on vision-based human action recognition publication-title: Image Vis. Comput. – start-page: 770 year: 2016 end-page: 778 ident: bb0240 article-title: Deep residual learning for image recognition publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – volume: 10 start-page: 343 year: 2016 end-page: 350 ident: bb0210 article-title: A modified gaussian mixture background model via spatiotemporal distribution with shadow detection publication-title: SIViP – volume: 27 start-page: 023002 year: 2018 ident: bb0305 article-title: Swcd: a sliding window and self-regulated learning-based background updating method for change detection in videos publication-title: J. Electron Imaging – volume: 12 year: 2022 ident: bb0390 article-title: End-to-end deep auto-encoder for segmenting a moving object with limited training data publication-title: Int. J. Electric. Comput. Eng. (2088–8708) – start-page: 4552 year: 2017 end-page: 4556 ident: bb0290 article-title: Semantic background subtraction publication-title: Proceedings of the IEEE International Conference on Image Processing – volume: 20 start-page: 1709 year: 2011 end-page: 1724 ident: bb0205 article-title: ViBe: a universal background subtraction algorithm for video sequences publication-title: IEEE Trans. Image Process. – start-page: 3244 year: 2023 end-page: 3255 ident: bb0395 article-title: Autoencoder-based background reconstruction and foreground segmentation with background noise estimation publication-title: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision – year: 2017 ident: bb0320 article-title: WiSARDrp for change detection in video sequences publication-title: Proceedings of the European Symposium on Artificial Neural Networks – start-page: 990 year: 2015 end-page: 997 ident: bb0315 article-title: A self-adjusting approach to change detection based on background word consensus publication-title: Proceedings of the IEEE Winter Conference on Applications of Computer Vision – volume: 222 start-page: 103501 year: 2022 ident: bb0055 article-title: Encoder and decoder network with ResNet-50 and global average feature pooling for local change detection publication-title: Comput. Vis. Image Underst. – start-page: 2375 year: 2010 end-page: 2380 ident: bb0225 article-title: Study on moving-objects detection technique in video surveillance system publication-title: Proceedings of the IEEE Chinese Control and Decision Conference – start-page: 734 year: 2018 end-page: 750 ident: bb0195 article-title: Cornernet: Detecting objects as paired keypoints publication-title: Proceedings of the European conference on computer vision (ECCV) – volume: 96 start-page: 66 year: 2017 end-page: 75 ident: bb0280 article-title: Interactive deep learning method for segmenting moving objects publication-title: Pattern Recogn. Lett. – start-page: 5272 year: 2019 end-page: 5277 ident: bb0140 article-title: An efficient optical flow based motion detection method for non-stationary scenes publication-title: Proceedings of the Chinese Control And Decision Conference – volume: vol. 1 year: 2001 ident: bb0105 article-title: Rapid object detection using a boosted cascade of simple features publication-title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition – volume: 7 start-page: 175 year: 2006 end-page: 187 ident: bb0020 article-title: Automatic traffic surveillance system for vehicle tracking and classification publication-title: IEEE Trans. Intell. Transp. Syst. – volume: 34 start-page: 743 year: 2012 end-page: 761 ident: bb0110 article-title: Pedestrian detection: an evaluation of the state of the art publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – start-page: 50 year: 2017 end-page: 57 ident: bb0330 article-title: Bmog: boosted gaussian mixture model with controlled complexity publication-title: Pattern Recognition and Image Analysis: 8th Iberian Conference, IbPRIA 2017, Faro, Portugal, June 20–23, 2017, Proceedings 8 – volume: 97 start-page: 117 year: 2018 end-page: 136 ident: bb0030 article-title: Spatio-contextual Gaussian mixture model for local change detection in underwater video publication-title: Expert Syst. Appl. – volume: 170 start-page: 1 year: 2021 end-page: 8 ident: bb0145 article-title: Optical-flow-based framework to boost video object detection performance with object enhancement publication-title: Expert Syst. Appl. – year: 2019 ident: bb0190 article-title: Objects as points – volume: 25 start-page: 1337 year: 2003 end-page: 1342 ident: bb0125 article-title: Detecting moving objects, ghosts, and shadows in video streams publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 7 start-page: 59143 year: 2019 end-page: 59171 ident: bb0040 article-title: A comprehensive survey of video datasets for background subtraction publication-title: IEEE Access – volume: 11 start-page: 407 year: 2017 end-page: 414 ident: bb0215 article-title: Background subtraction based on circulant matrix publication-title: SIViP – start-page: 2774 year: 2020 end-page: 2783 ident: bb0265 article-title: BSUV-Net: A fully-convolutional neural network for background subtraction of unseen videos publication-title: Proceedings of the IEEE Winter Conference on Applications of Computer Vision – volume: 66 start-page: 7155 year: 2020 end-page: 7179 ident: bb0200 article-title: Deconstructing generative adversarial networks publication-title: IEEE Trans. Inf. Theory – start-page: 1 year: 2022 end-page: 8 ident: bb0285 article-title: An end to end encoder-decoder network with multi-scale feature pulling for detecting local changes from video scene publication-title: Proceedings of the 18th IEEE International Conference on Advanced Video and Signal Based Surveillance – volume: 394 start-page: 178 year: 2020 end-page: 200 ident: bb0260 article-title: A novel background subtraction algorithm based on parallel vision and Bayesian GANs publication-title: Neurocomputing – volume: 13 start-page: 719 year: 2019 end-page: 729 ident: bb0310 article-title: Cvabs: moving object segmentation with common vector approach for videos publication-title: IET Comput. Vis. – volume: 26 start-page: 3249 year: 2017 end-page: 3260 ident: bb0325 article-title: Universal multimode background subtraction publication-title: IEEE Trans. Image Process. – volume: 6 start-page: 15505 year: 2018 end-page: 15520 ident: bb0345 article-title: : a robust change detection method for intelligent visual surveillance publication-title: IEEE Access – volume: 168 start-page: 605 year: 2018 end-page: 618 ident: bb0165 article-title: Moving object detection using an adaptive background subtraction method based on block-based structure in dynamic scene publication-title: Optik – start-page: 21 year: 2016 end-page: 37 ident: bb0175 article-title: Ssd: Single shot multibox detector publication-title: Computer Vision – ECCV 2016 – volume: 14 start-page: 346 year: 1992 end-page: 352 ident: bb0095 article-title: On the detection of motion and the computation of optical flow publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – start-page: 135 year: 2002 end-page: 144 ident: bb0135 article-title: An Improved Adaptive Background Mixture Model for Real-Time Tracking with Shadow Detection – volume: 34 start-page: 334 year: 2004 end-page: 352 ident: bb0025 article-title: A survey on visual surveillance of object motion and behaviors publication-title: IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. – start-page: 266 year: 2019 ident: bb0300 article-title: An end-to-end deep learning approach for simultaneous background modeling and subtraction publication-title: Proceedings of the British Machine Vision Conference – volume: 11 start-page: 1357 year: 2017 end-page: 1364 ident: bb0155 article-title: Moving object detection based on frame difference and w4 publication-title: SIViP – volume: 234 start-page: 11 year: 2017 end-page: 26 ident: bb0060 article-title: A survey of deep neural network architectures and their applications publication-title: Neurocomputing – volume: 12 start-page: 1265 year: 2018 end-page: 1272 ident: bb0120 article-title: A fast valley-based segmentation for detection of slowly moving objects publication-title: SIViP – volume: 1 start-page: 492 year: 2012 end-page: 497 ident: bb0160 article-title: Moving-object detection based on sparse representation and dictionary learning publication-title: AASRI Procedia – volume: 24 start-page: 411 year: 2006 end-page: 423 ident: bb0230 article-title: Moving object segmentation by background subtraction and temporal analysis publication-title: Image Vis. Comput. – start-page: 6355 year: 2023 end-page: 6364 ident: bb0365 article-title: Zbs: Zero-shot background subtraction via instance-level background modeling and foreground selection publication-title: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition – start-page: 1 year: 2022 end-page: 12 ident: bb0045 article-title: Kernel-induced possibilistic fuzzy associate background subtraction for video scene publication-title: IEEE Trans. Comput. Soc. Syst. – volume: 117 start-page: 8 year: 2019 end-page: 66 ident: bb0065 article-title: Deep neural network concepts for background subtraction: a systematic review and comparative evaluation publication-title: Neural Netw. – year: 2000 ident: bb0080 article-title: Yuv video sequences – volume: 28 start-page: 2105 year: 2017 end-page: 2115 ident: bb0335 article-title: WeSamBE: a weight-sample-based method for background subtraction publication-title: IEEE Trans. Circuits Syst. Video Technol. – start-page: 3214 year: 2020 end-page: 3218 ident: bb0340 article-title: Real-time semantic background subtraction publication-title: Proceedings of the IEEE International Conference on Image Processing – year: 2016 ident: bb0180 article-title: Faster r-cnn: Towards real-time object detection with region proposal networks – start-page: 1 year: 2014 end-page: 5 ident: bb0090 article-title: Three frame based adaptive background subtraction publication-title: Proceedings of the International Conference on High Performance Computing and Applications – year: 2004 ident: bb0085 article-title: Xiph.org video test media [derf’s collection] – volume: 34 start-page: 5296 year: 2022 end-page: 5304 ident: bb0220 article-title: Entropy feature and peak-means clustering based slowly moving object detection in head and shoulder video sequences publication-title: J. King Saud Univ. Comput. Inform. Sci. – start-page: 387 year: 2014 end-page: 394 ident: bb0075 article-title: Cdnet 2014: An expanded change detection benchmark dataset publication-title: Proceedings of the IEEE conference on computer vision and pattern recognition workshops – start-page: 1 year: 2008 end-page: 6 ident: bb0245 article-title: Detection of slow moving video objects using compound markov random field model publication-title: TENCON 2008–2008 IEEE Region 10 Conference – volume: 11 start-page: 1357 year: 2017 ident: 10.1016/j.imavis.2024.105021_bb0155 article-title: Moving object detection based on frame difference and w4 publication-title: SIViP doi: 10.1007/s11760-017-1093-8 – start-page: 734 year: 2018 ident: 10.1016/j.imavis.2024.105021_bb0195 article-title: Cornernet: Detecting objects as paired keypoints – start-page: 1 year: 2022 ident: 10.1016/j.imavis.2024.105021_bb0285 article-title: An end to end encoder-decoder network with multi-scale feature pulling for detecting local changes from video scene – volume: 11 start-page: 31 year: 2014 ident: 10.1016/j.imavis.2024.105021_bb0050 article-title: Traditional and recent approaches in background modeling for foreground detection: an overview publication-title: Comput. Sci. Rev. doi: 10.1016/j.cosrev.2014.04.001 – volume: 222 start-page: 103501 year: 2022 ident: 10.1016/j.imavis.2024.105021_bb0055 article-title: Encoder and decoder network with ResNet-50 and global average feature pooling for local change detection publication-title: Comput. Vis. Image Underst. doi: 10.1016/j.cviu.2022.103501 – volume: 242 issue: 1 year: 2017 ident: 10.1016/j.imavis.2024.105021_bb0150 article-title: A new moving object detection method based on frame-difference and background subtraction publication-title: IOP Conf. Ser. Mater. Sci. Eng. – volume: 168 start-page: 605 year: 2018 ident: 10.1016/j.imavis.2024.105021_bb0165 article-title: Moving object detection using an adaptive background subtraction method based on block-based structure in dynamic scene publication-title: Optik doi: 10.1016/j.ijleo.2018.04.047 – volume: 117 start-page: 8 year: 2019 ident: 10.1016/j.imavis.2024.105021_bb0065 article-title: Deep neural network concepts for background subtraction: a systematic review and comparative evaluation publication-title: Neural Netw. doi: 10.1016/j.neunet.2019.04.024 – volume: 24 start-page: 411 issue: 5 year: 2006 ident: 10.1016/j.imavis.2024.105021_bb0230 article-title: Moving object segmentation by background subtraction and temporal analysis publication-title: Image Vis. Comput. doi: 10.1016/j.imavis.2006.01.001 – volume: 27 start-page: 023002 issue: 2 year: 2018 ident: 10.1016/j.imavis.2024.105021_bb0305 article-title: Swcd: a sliding window and self-regulated learning-based background updating method for change detection in videos publication-title: J. Electron Imaging doi: 10.1117/1.JEI.27.2.023002 – volume: vol. 1 start-page: 255 year: 1999 ident: 10.1016/j.imavis.2024.105021_bb0380 article-title: Wallflower: Principles and practice of background maintenance – volume: 25 start-page: 1337 issue: 10 year: 2003 ident: 10.1016/j.imavis.2024.105021_bb0125 article-title: Detecting moving objects, ghosts, and shadows in video streams publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2003.1233909 – volume: 26 start-page: 3249 issue: 7 year: 2017 ident: 10.1016/j.imavis.2024.105021_bb0325 article-title: Universal multimode background subtraction publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2695882 – volume: 14 start-page: 346 issue: 03 year: 1992 ident: 10.1016/j.imavis.2024.105021_bb0095 article-title: On the detection of motion and the computation of optical flow publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.120329 – volume: 34 start-page: 5296 issue: 8 year: 2022 ident: 10.1016/j.imavis.2024.105021_bb0220 article-title: Entropy feature and peak-means clustering based slowly moving object detection in head and shoulder video sequences publication-title: J. King Saud Univ. Comput. Inform. Sci. – start-page: 1 year: 2023 ident: 10.1016/j.imavis.2024.105021_bb0005 article-title: Modified ResNet-152 network with hybrid pyramidal pooling for local change detection publication-title: IEEE Trans. Artif. Intell. – volume: 6 start-page: 43450 year: 2018 ident: 10.1016/j.imavis.2024.105021_bb0035 article-title: A 3D Atrous convolutional long short-term memory network for background subtraction publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2861223 – year: 2019 ident: 10.1016/j.imavis.2024.105021_bb0190 – volume: 11 start-page: 407 year: 2017 ident: 10.1016/j.imavis.2024.105021_bb0215 article-title: Background subtraction based on circulant matrix publication-title: SIViP doi: 10.1007/s11760-016-0975-5 – start-page: 770 year: 2016 ident: 10.1016/j.imavis.2024.105021_bb0240 article-title: Deep residual learning for image recognition – start-page: 3244 year: 2023 ident: 10.1016/j.imavis.2024.105021_bb0395 article-title: Autoencoder-based background reconstruction and foreground segmentation with background noise estimation – volume: 96 start-page: 66 year: 2017 ident: 10.1016/j.imavis.2024.105021_bb0280 article-title: Interactive deep learning method for segmenting moving objects publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2016.09.014 – year: 2000 ident: 10.1016/j.imavis.2024.105021_bb0080 – start-page: 1 year: 2008 ident: 10.1016/j.imavis.2024.105021_bb0245 article-title: Detection of slow moving video objects using compound markov random field model – volume: 6 start-page: 15505 year: 2018 ident: 10.1016/j.imavis.2024.105021_bb0345 article-title: M4CD: a robust change detection method for intelligent visual surveillance publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2812880 – start-page: 1 year: 2014 ident: 10.1016/j.imavis.2024.105021_bb0090 article-title: Three frame based adaptive background subtraction – volume: 1 start-page: 492 year: 2012 ident: 10.1016/j.imavis.2024.105021_bb0160 article-title: Moving-object detection based on sparse representation and dictionary learning publication-title: AASRI Procedia doi: 10.1016/j.aasri.2012.06.077 – volume: 28 start-page: 976 issue: 6 year: 2010 ident: 10.1016/j.imavis.2024.105021_bb0015 article-title: A survey on vision-based human action recognition publication-title: Image Vis. Comput. doi: 10.1016/j.imavis.2009.11.014 – volume: 13 start-page: 555 issue: 4 year: 2023 ident: 10.1016/j.imavis.2024.105021_bb0235 article-title: Lie recognition with multi-modal spatial–temporal state transition patterns based on hybrid convolutional neural network–bidirectional long short-term memory publication-title: Brain Sci. doi: 10.3390/brainsci13040555 – volume: vol. 104 year: 2016 ident: 10.1016/j.imavis.2024.105021_bb0355 – start-page: 1 year: 2022 ident: 10.1016/j.imavis.2024.105021_bb0045 article-title: Kernel-induced possibilistic fuzzy associate background subtraction for video scene publication-title: IEEE Trans. Comput. Soc. Syst. – start-page: 2375 year: 2010 ident: 10.1016/j.imavis.2024.105021_bb0225 article-title: Study on moving-objects detection technique in video surveillance system – start-page: 779 year: 2016 ident: 10.1016/j.imavis.2024.105021_bb0115 article-title: You only look once: Unified, real-time object detection – start-page: 50 year: 2017 ident: 10.1016/j.imavis.2024.105021_bb0330 article-title: Bmog: boosted gaussian mixture model with controlled complexity – volume: 97 start-page: 117 year: 2018 ident: 10.1016/j.imavis.2024.105021_bb0030 article-title: Spatio-contextual Gaussian mixture model for local change detection in underwater video publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2017.12.009 – start-page: 4552 year: 2017 ident: 10.1016/j.imavis.2024.105021_bb0290 article-title: Semantic background subtraction – volume: 20 start-page: 1709 issue: 6 year: 2011 ident: 10.1016/j.imavis.2024.105021_bb0205 article-title: ViBe: a universal background subtraction algorithm for video sequences publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2010.2101613 – year: 2004 ident: 10.1016/j.imavis.2024.105021_bb0085 – volume: 34 start-page: 334 issue: 3 year: 2004 ident: 10.1016/j.imavis.2024.105021_bb0025 article-title: A survey on visual surveillance of object motion and behaviors publication-title: IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. doi: 10.1109/TSMCC.2004.829274 – start-page: 135 year: 2002 ident: 10.1016/j.imavis.2024.105021_bb0135 – volume: 66 start-page: 249 issue: 3 year: 2012 ident: 10.1016/j.imavis.2024.105021_bb0250 article-title: A hybrid algorithm for automatic segmentation of slowly moving objects publication-title: AEU Int. J. Electron. Commun. doi: 10.1016/j.aeue.2011.07.009 – start-page: 2774 year: 2020 ident: 10.1016/j.imavis.2024.105021_bb0265 article-title: BSUV-Net: A fully-convolutional neural network for background subtraction of unseen videos – start-page: 107 year: 2014 ident: 10.1016/j.imavis.2024.105021_bb0375 article-title: Dynamic background learning through deep auto-encoder networks – volume: vol. 1 year: 2001 ident: 10.1016/j.imavis.2024.105021_bb0105 article-title: Rapid object detection using a boosted cascade of simple features – volume: 13 start-page: 719 issue: 8 year: 2019 ident: 10.1016/j.imavis.2024.105021_bb0310 article-title: Cvabs: moving object segmentation with common vector approach for videos publication-title: IET Comput. Vis. doi: 10.1049/iet-cvi.2018.5642 – volume: 32 start-page: 2145 issue: 4 year: 2021 ident: 10.1016/j.imavis.2024.105021_bb0360 article-title: Stpnet: a spatial-temporal propagation network for background subtraction publication-title: IEEE Trans. Circuits Syst. Video Technol. doi: 10.1109/TCSVT.2021.3088130 – start-page: 990 year: 2015 ident: 10.1016/j.imavis.2024.105021_bb0315 article-title: A self-adjusting approach to change detection based on background word consensus – volume: 394 start-page: 178 year: 2020 ident: 10.1016/j.imavis.2024.105021_bb0260 article-title: A novel background subtraction algorithm based on parallel vision and Bayesian GANs publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.04.088 – volume: 234 start-page: 11 year: 2017 ident: 10.1016/j.imavis.2024.105021_bb0060 article-title: A survey of deep neural network architectures and their applications publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.12.038 – year: 2016 ident: 10.1016/j.imavis.2024.105021_bb0180 – volume: 28 start-page: 1750056 issue: 05 year: 2018 ident: 10.1016/j.imavis.2024.105021_bb0350 article-title: Foreground detection by competitive learning for varying input distributions publication-title: Int. J. Neural Syst. doi: 10.1142/S0129065717500563 – volume: 4 start-page: 6133 year: 2016 ident: 10.1016/j.imavis.2024.105021_bb0100 article-title: An evaluation of background subtraction for object detection vis-a-vis mitigating challenging scenarios publication-title: IEEE Access doi: 10.1109/ACCESS.2016.2608847 – volume: 35 start-page: 1285 issue: 5 year: 2016 ident: 10.1016/j.imavis.2024.105021_bb0070 article-title: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2016.2528162 – volume: 12 start-page: 1265 year: 2018 ident: 10.1016/j.imavis.2024.105021_bb0120 article-title: A fast valley-based segmentation for detection of slowly moving objects publication-title: SIViP doi: 10.1007/s11760-018-1278-9 – start-page: 21 year: 2016 ident: 10.1016/j.imavis.2024.105021_bb0175 article-title: Ssd: Single shot multibox detector – volume: 9 start-page: 53849 year: 2021 ident: 10.1016/j.imavis.2024.105021_bb0255 article-title: Bsuv-net 2.0: spatio-temporal data augmentations for video-agnostic supervised background subtraction publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3071163 – start-page: 433 year: 2016 ident: 10.1016/j.imavis.2024.105021_bb0370 article-title: C-efic: Color and edge based foreground background segmentation with interior classification – volume: 7 start-page: 59143 year: 2019 ident: 10.1016/j.imavis.2024.105021_bb0040 article-title: A comprehensive survey of video datasets for background subtraction publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2914961 – start-page: 5272 year: 2019 ident: 10.1016/j.imavis.2024.105021_bb0140 article-title: An efficient optical flow based motion detection method for non-stationary scenes – volume: 76 start-page: 635 year: 2018 ident: 10.1016/j.imavis.2024.105021_bb0275 article-title: A deep convolutional neural network for video sequence background subtraction publication-title: Pattern Recogn. doi: 10.1016/j.patcog.2017.09.040 – volume: 7 start-page: 175 issue: 2 year: 2006 ident: 10.1016/j.imavis.2024.105021_bb0020 article-title: Automatic traffic surveillance system for vehicle tracking and classification publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2006.874722 – volume: 31 start-page: 1804 issue: 5 year: 2021 ident: 10.1016/j.imavis.2024.105021_bb0170 article-title: Revisiting feature fusion for rgb-t salient object detection publication-title: IEEE Trans. Circuits Syst. Video Technol. doi: 10.1109/TCSVT.2020.3014663 – start-page: 1 year: 2017 ident: 10.1016/j.imavis.2024.105021_bb0130 article-title: Neighborhood based codebook model for moving object segmentation – volume: 21 start-page: 914 issue: 6 year: 2017 ident: 10.1016/j.imavis.2024.105021_bb0295 article-title: Combination of video change detection algorithms by genetic programming publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2017.2694160 – volume: 28 start-page: 2105 issue: 9 year: 2017 ident: 10.1016/j.imavis.2024.105021_bb0335 article-title: WeSamBE: a weight-sample-based method for background subtraction publication-title: IEEE Trans. Circuits Syst. Video Technol. doi: 10.1109/TCSVT.2017.2711659 – volume: 79 start-page: 4639 year: 2020 ident: 10.1016/j.imavis.2024.105021_bb0385 article-title: Dynamic background modeling using deep learning autoencoder network publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-019-7411-0 – volume: 170 start-page: 1 year: 2021 ident: 10.1016/j.imavis.2024.105021_bb0145 article-title: Optical-flow-based framework to boost video object detection performance with object enhancement publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2020.114544 – volume: 11 start-page: 1 issue: 5 year: 2019 ident: 10.1016/j.imavis.2024.105021_bb0270 article-title: WisenetMD: motion detection using dynamic background region analysis publication-title: Symmetry doi: 10.3390/sym11050621 – volume: 66 start-page: 7155 issue: 11 year: 2020 ident: 10.1016/j.imavis.2024.105021_bb0200 article-title: Deconstructing generative adversarial networks publication-title: IEEE Trans. Inf. Theory doi: 10.1109/TIT.2020.2983698 – year: 2017 ident: 10.1016/j.imavis.2024.105021_bb0320 article-title: WiSARDrp for change detection in video sequences – volume: 12 issue: 6 year: 2022 ident: 10.1016/j.imavis.2024.105021_bb0390 article-title: End-to-end deep auto-encoder for segmenting a moving object with limited training data publication-title: Int. J. Electric. Comput. Eng. (2088–8708) – volume: 34 start-page: 743 issue: 4 year: 2012 ident: 10.1016/j.imavis.2024.105021_bb0110 article-title: Pedestrian detection: an evaluation of the state of the art publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2011.155 – start-page: 6355 year: 2023 ident: 10.1016/j.imavis.2024.105021_bb0365 article-title: Zbs: Zero-shot background subtraction via instance-level background modeling and foreground selection – start-page: 387 year: 2014 ident: 10.1016/j.imavis.2024.105021_bb0075 article-title: Cdnet 2014: An expanded change detection benchmark dataset – start-page: 266 year: 2019 ident: 10.1016/j.imavis.2024.105021_bb0300 article-title: An end-to-end deep learning approach for simultaneous background modeling and subtraction – start-page: 3214 year: 2020 ident: 10.1016/j.imavis.2024.105021_bb0340 article-title: Real-time semantic background subtraction – volume: 23 start-page: 2031 issue: 3 year: 2020 ident: 10.1016/j.imavis.2024.105021_bb0010 article-title: Scene independency matters: an empirical study of scene dependent and scene independent evaluation for CNN-based change detection publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2020.3030801 – year: 2017 ident: 10.1016/j.imavis.2024.105021_bb0185 article-title: Focal loss for dense object detection – volume: 10 start-page: 343 year: 2016 ident: 10.1016/j.imavis.2024.105021_bb0210 article-title: A modified gaussian mixture background model via spatiotemporal distribution with shadow detection publication-title: SIViP doi: 10.1007/s11760-014-0747-z |
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| Snippet | Background subtraction is a crucial stage in many visual surveillance systems. The prime objective of any such system is to detect moving objects such that the... |
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| SubjectTerms | Background subtraction Contrast normalization Deep learning architecture Feature pooling framework Transfer learning |
| Title | A ResNet-101 deep learning framework induced transfer learning strategy for moving object detection |
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