Robust and efficient FISTA-based method for moving object detection under background movements
Moving object detection is a fundamental task in many video processing applications, such as video surveillance. The robustness and efficiency of background subtraction make it one of the most common methods for detecting moving objects from a video stream. However, adapting background models with m...
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| Vydáno v: | Knowledge-based systems Ročník 294; s. 111765 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
21.06.2024
Elsevier |
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| ISSN: | 0950-7051, 1872-7409 |
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| Abstract | Moving object detection is a fundamental task in many video processing applications, such as video surveillance. The robustness and efficiency of background subtraction make it one of the most common methods for detecting moving objects from a video stream. However, adapting background models with moving cameras remains challenging. Major issues include maintaining the model given viewpoint alterations and compensating motions considering depth variations. Moreover, gradual illumination changes, dynamic backgrounds, and complex motions intensify over time in moving camera scenarios, further complicating background model maintenance. In this context, this paper proposes a novel Robust and Online Tensor-based model named ROTAB that incorporates a more implicit consideration of the relationship between sequential frames than the previous methods, allowing for better adaptation to background changes. Moreover, we propose an Improved version of FISTA named IFISTA that employs two strategies to reduce oscillatory behavior and minimize iterations, improving stability and efficiency. Practically, the combination of IFISTA and ROTAB (IFISTA-ROTAB) demonstrates suitable performance for real-time applications. Quantitative and qualitative experiments are conducted on three large-scale datasets, namely CDnet 2014, BMC 2012 and LASIESTA showing the superiority of IFISTA-ROTAB with a gain from two up to seven percent in average. |
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| AbstractList | Moving object detection is a fundamental task in many video processing applications, such as video surveillance. The robustness and efficiency of background subtraction make it one of the most common methods for detecting moving objects from a video stream. However, adapting background models with moving cameras remains challenging. Major issues include maintaining the model given viewpoint alterations and compensating motions considering depth variations. Moreover, gradual illumination changes, dynamic backgrounds, and complex motions intensify over time in moving camera scenarios, further complicating background model maintenance. In this context, this paper proposes a novel Robust and Online Tensor-based model named ROTAB that incorporates a more implicit consideration of the relationship between sequential frames than the previous methods, allowing for better adaptation to background changes. Moreover, we propose an Improved version of FISTA named IFISTA that employs two strategies to reduce oscillatory behavior and minimize iterations, improving stability and efficiency. Practically, the combination of IFISTA and ROTAB (IFISTA-ROTAB) demonstrates suitable performance for real-time applications. Quantitative and qualitative experiments are conducted on three large-scale datasets, namely CDnet 2014, BMC 2012 and LASIESTA showing the superiority of IFISTA-ROTAB with a gain from two up to seven percent in average. Moving object detection is a fundamental task in many video processing applications, such as video surveillance. The robustness and efficiency of background subtraction make it one of the most common methods for detecting moving objects from a video stream. However, adapting background models with moving cameras remains challenging. Main issues include maintaining the model given viewpoint alterations and compensating motions considering depth variations. Moreover, gradual illumination changes, dynamic backgrounds, and complex motions intensify over time in moving camera scenarios, further complicating background model maintenance. In this context, this paper proposes a novel Robust and Online Tensor-based model named ROTAB that incorporates a more implicit consideration of the relationship between sequential frames than the previous methods, allowing for better adaptation to background changes. Moreover, we propose an Improved version of FISTA named IFISTA that employs two strategies to reduce oscillatory behavior and minimize iterations, improving stability and efficiency. Practically, the combination of IFISTA and ROTAB (IFISTA-ROTAB) demonstrates suitable performance for real-time applications. Quantitative and qualitative experiments are conducted on three large-scale datasets, namely CDnet 2014, BMC 2012 and LASIESTA showing the superiority of IFISTA-ROTAB with a gain from two up to seven percent in average. |
| ArticleNumber | 111765 |
| Author | Akbarizadeh, Masoumeh Amoozegar, Maryam Bouwmans, Thierry |
| Author_xml | – sequence: 1 givenname: Maryam orcidid: 0000-0001-7161-8623 surname: Amoozegar fullname: Amoozegar, Maryam email: moozegar@kgut.ac.ir organization: Department of Computer and Information Technology, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran – sequence: 2 givenname: Masoumeh surname: Akbarizadeh fullname: Akbarizadeh, Masoumeh email: masoumeh.akbarizadeh@gmail.com organization: Department of Mathematics, Iran University of Science and Technology. Narmak, Tehran 16846-13114, Iran – sequence: 3 givenname: Thierry orcidid: 0000-0003-4018-8856 surname: Bouwmans fullname: Bouwmans, Thierry email: thierry.bouwmans@univ-lr.fr organization: Laboratoire MIA at La Rochelle Universite, France |
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| Keywords | Fast iterative Shrinkage-Thresholding algorithm Background subtraction Moving object detection Online learning Robust PCA Foreground Detection Background Subtraction Background Modeling |
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