Motion cues and saliency based unconstrained video segmentation
The segmentation of moving objects become challenging when the object motion is small, the shape of object changes, and there is global background motion in unconstrained videos. In this paper, we propose a fully automatic, efficient, fast and composite framework to segment the moving object on the...
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| Veröffentlicht in: | Multimedia tools and applications Jg. 77; H. 6; S. 7429 - 7446 |
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| Abstract | The segmentation of moving objects become challenging when the object motion is small, the shape of object changes, and there is global background motion in unconstrained videos. In this paper, we propose a fully automatic, efficient, fast and composite framework to segment the moving object on the basis of saliency, locality, color and motion cues. First, we propose a new saliency measure to predict the potential salient regions. In the second step, we use the RANSAC homography and optical flow to compensate the background motion and get reliable motion information, called motion cues. Furthermore, the saliency information and motion cues are combined to get the initial segmented object (seeded region). A refinement is performed to remove the unwanted noisy details and expand the seeded region to the whole object. Detailed experimentation is carried out on challenging video benchmarks to evaluate the performance of the proposed method. The results show that the proposed method is faster and performs better than state-of-the-art approaches. |
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| AbstractList | The segmentation of moving objects become challenging when the object motion is small, the shape of object changes, and there is global background motion in unconstrained videos. In this paper, we propose a fully automatic, efficient, fast and composite framework to segment the moving object on the basis of saliency, locality, color and motion cues. First, we propose a new saliency measure to predict the potential salient regions. In the second step, we use the RANSAC homography and optical flow to compensate the background motion and get reliable motion information, called motion cues. Furthermore, the saliency information and motion cues are combined to get the initial segmented object (seeded region). A refinement is performed to remove the unwanted noisy details and expand the seeded region to the whole object. Detailed experimentation is carried out on challenging video benchmarks to evaluate the performance of the proposed method. The results show that the proposed method is faster and performs better than state-of-the-art approaches. |
| Author | Khan, Ahmad Jaffar, Muhammad Arfan Ullah, Javid |
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| Keywords | Saliency Moving object Homography Segmentation Optical flow Recognition |
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In: European Conference on Computer Vision A Khan (4655_CR23) 2014; 8 4655_CR18 4655_CR16 4655_CR15 4655_CR19 4655_CR53 4655_CR52 4655_CR51 4655_CR14 T Brox (4655_CR3) 2010; 33 4655_CR13 4655_CR12 4655_CR11 4655_CR50 P KaewTraKulPong (4655_CR21) 2001; 1 4655_CR29 4655_CR28 4655_CR27 4655_CR26 4655_CR20 4655_CR25 4655_CR24 4655_CR22 E Rubio (4655_CR41) 2011; 115 T Matsuyama (4655_CR32) 2006; 37 E Rubio (4655_CR42) 2011; 21 M Fischler (4655_CR10) 1981; 24 4655_CR38 4655_CR37 4655_CR1 4655_CR31 4655_CR30 4655_CR2 4655_CR36 4655_CR35 4655_CR34 4655_CR33 V Reddy (4655_CR39) 2013; 23 4655_CR9 4655_CR5 4655_CR4 4655_CR7 4655_CR6 4655_CR49 4655_CR48 4655_CR43 M Heikkila (4655_CR17) 2006; 28 4655_CR40 4655_CR47 4655_CR46 4655_CR45 4655_CR44 A Elgammal (4655_CR8) 2002; 90 |
| References_xml | – reference: Ochs T, Brox P (2012) Higher order motion models and spectral clustering. IEEE Conference on Computer Vision and Pattern Recognition – reference: Liu C (2009) Beyond pixels: Exploring new representations and applications for motion analysis. In: Doctoral Thesis. Massachusetts Institute of Technology – reference: Liu F, Gleicher M (2009) Learning color and locality cues for moving object detection and segmentation. IEEE Conference on Computer Vision and Pattern Recognition – reference: Chockalingam P, Pradeep N, Birchfield S (2009) Adaptive fragments-based tracking of non-rigid objects using level sets. In: BMVC, pp 1530–1537 – reference: Fragkiadaki K, Arbelaez P, Felsen P, Malik J (2015) Learning to segment moving objects in videos. IEEE Conference on Computer Vision and Pattern Recognition, 4083–4090 – reference: Bugeau A, Perez P (2007) Detection and segmentation of moving objects in highly dynamic scenes. IEEE Conference on Computer Vision and Pattern Recognition, 1–8 – reference: Khan S, Shah M (2001) Object based segmentation of video using color, motion and spatial information. IEEE Conference on Computer Vision and Pattern Recognition, 746–751 – reference: Chiu W, Fritz M (2013) Multi-class video co-segmentation with a generative multi-video model. IEEE Conference on Computer Vision and Pattern Recognition – reference: Bai X, Wang J, Simons D, Sapiro G (2009) Video snapcut: robust video object cutout using localized classifiers. ACM Transactions on Graphics, 28(3) – reference: Mahamud S (2006) Comparing belief propagation and graph cuts for novelty detection. IEEE Conference on Computer Vision and Pattern Recognition, 1154–1159 – reference: KhanAUllahJJaffarMColor imagesegmentation: A novel spatial fuzzy genetic algorithmSIViP, Springer2014871233124310.1007/s11760-012-0347-8 – reference: Wang Y, Ji Q (2005) A dynamic conditional random field model for object segmentation in image sequences. In: Proceedings of IEEE CVPR, pp 264–270 – reference: Han M, Xu W, Gong Y (2006) Video object segmentation by motion-based sequential feature clustering. In: ACM Multimedia, pp 773–782 – reference: Brox T, Malik J (2010) Object segmentation by long term analysis of point trajectories. In: European Conference on Computer Vision – reference: Price B, Morse B, Cohen S (2009) Livecut: Learning-based interactive video segmentation by evaluation of multiple propagated cues. IEEE Conference on Computer Vision, 779–786 – reference: Liu C, Yuen J, Russell B, Torralba A (2009) Labelme video: Building a video database with human annotations. 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