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http://dx.doi.org/10.3837/tiis.2016.06.012

Unsupervised Single Moving Object Detection Based on Coarse-to-Fine Segmentation  

Zhu, Xiaozhou (College of Aerospace Science and Engineering, National University of Defense Technology)
Song, Xin (College of Aerospace Science and Engineering, National University of Defense Technology)
Chen, Xiaoqian (College of Aerospace Science and Engineering, National University of Defense Technology)
Lu, Huimin (College of Mechatronics and Automation, National University of Defense Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.10, no.6, 2016 , pp. 2669-2688 More about this Journal
Abstract
An efficient and effective unsupervised single moving object detection framework is presented in this paper. Given the sparsely labelled trajectory points, we adopt a coarse-to-fine strategy to detect and segment the foreground from the background. The superpixel level coarse segmentation reduces the complexity of subsequent processing, and the pixel level refinement improves the segmentation accuracy. A distance measurement is devised in the coarse segmentation stage to measure the similarities between generated superpixels, which can then be used for clustering. Moreover, a Quadmap is introduced to facilitate the refinement in the fine segmentation stage. According to the experiments, our algorithm is effective and efficient, and favorable results can be achieved compared with state-of-the-art methods.
Keywords
Moving object detection; motion segmentation; superpixel; spectral clustering;
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1 C. Stauffer, W. E. L. Grimson, "Adaptive background mixture models for real-time tracking," in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2246-2252, June 23-25, 1999. Article (CrossRef Link)
2 K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, "Background modeling and subtraction by codebook construction," in Proc. of the IEEE International Conference on Image Processing, pp. 3061-3064, October 24-27, 2004. Article (CrossRef Link)
3 M. Heikkilä, M. Pietikäinen, “A texture-based method for modeling the background and detecting moving objects,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 4, pp. 657-662, May, 2006. Article (CrossRef Link)   DOI
4 S. Z. Li, M. Pietikainen, V. Kellokumpu, G. Zhao, and S. Liao, "Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes," in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1301-1306, June 13-18, 2010. Article (CrossRef Link)
5 R. K. Sabhara, C. P. Lee and K. M. Lim, “Comparative Study of Hu Moments and Zernike Moments in Object Recognition,” Smart Computing Review, Vol. 3, No. 3, pp. 166-173, June 2013. Article (CrossRef Link)   DOI
6 S. Brutzer, B. Höferlin, G. Heidemann, "Evaluation of background subtraction techniques for video surveillance," in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1937-1944, June 20-25, 2011. Article (CrossRef Link)
7 O. Barnich, and M. V. Droogenbroeck, “Vibe: A universal background subtraction algorithm for video sequences,” IEEE Transactions on Image Processing, Vol. 20, No. 6, pp. 1709-1724, June, 2011. Article (CrossRef Link)   DOI
8 A. Papazoglou, V. Ferrari, "Fast object segmentation in unconstrained video," in Proc. of the IEEE International Conference on Computer Vision, pp. 1777-1784, December 1-8, 2013. Article (CrossRef Link)
9 C. Tomasi, and T. Kanade, “Shape and motion from image streams under orthography: A factorization method,” International Journal of Computer Vision, Vol. 9, No. 2, pp. 137-154, January, 1992. Article (CrossRef Link)   DOI
10 Y. Sheikh, O. Javed, and T. Kanade, "Background subtraction for freely moving cameras," in Proc. of the IEEE International Conference on Computer Vision, pp. 1219-1225, September 27 - October 4, 2009. Article (CrossRef Link)
11 A. Petit, Robust visual detection and tracking of complex objects: Applications to space autonomous rendezvous and proximity operations, Université de Rennes 1, Rennes, France, 2013. Article (CrossRef Link)
12 L. Zappella; X. Lladó; E. Provenzi and J. Salvi, “Enhanced local subspace affinity for feature-based motion segmentation,” Pattern Recognition. Vol. 44, No. 2, pp. 454-470, February, 2011. Article (CrossRef Link)   DOI
13 E. Elhamifar, R. Vidal, “Sparse subspace clustering: Algorithm, theory, and applications,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, No. 11, pp. 2765-2781, March, 2013. Article (CrossRef Link)   DOI
14 P. Ochs, J. Malik, and T.Brox, “Segmentation of moving objects by long term video analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 36, No. 6, pp. 1187-1200, 2014. Article (CrossRef Link)   DOI
15 F. Galasso, M. Keuper, T. Brox, and B. Schiele, "Spectral graph reduction for efficient image and streaming video segmentation," in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 49-56, June 23-28, 2014. Article (CrossRef Link)
16 F. Galasso, R. Cipolla, and B. Schiele, "Video segmentation with superpixels," in Proc. of the Asian Conference on Computer Vision, pp. 760-774, November 5-9, 2012. Article (CrossRef Link)
17 L. Ellis, and V. Zografos, "Online learning for fast segmentation of moving objects," in Proc. of the Asian Conference on Computer Vision, pp. 52-65, November 5-9, 2012. Article (CrossRef Link)
18 T. Brox, and J. Malik, "Object segmentation by long term analysis of point trajectories," in Proc. of the European Conference on Computer Vision, pp. 282-295, September 5-11, 2010. Article (CrossRef Link)
19 X. Ren, and J. Malik, "Learning a classification model for segmentation," in Proc. of the IEEE International Conference on Computer Vision, pp. 10-17, October 14-17, 2003. Article (CrossRef Link)
20 D. Stutz, Superpixel segmentation using depth information, RWTH Aachen University, Aachen, Germany, 2014. Article (CrossRef Link)
21 P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, “Contour detection and hierarchical image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 5, pp. 898-916, 2011. Article (CrossRef Link)   DOI
22 X. Z. Zhu, X. Song, X. Q. Chen, and H. M. Lu, "Flying spacecraft detection with the earth as the background based on superpixels clustering," in Proc. of the IEEE International Conference on Information and Automation, pp. 518-523, August 8-10, 2015. Article (CrossRef Link)
23 J. Shi, and C. Tomasi, "Good features to track," in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 593-600, June 21-23, 1994. Article (CrossRef Link)
24 A. Y. Ng, M. I. Jordan, and Y. Weiss, "On spectral clustering: Analysis and an algorithm," in Proc. of Advances in Neural Information Processing Systems, pp. 849-856, December 3-8, 2001. Article (CrossRef Link)
25 U.v. Luxburg, “A tutorial on spectral clustering,” Statistics and Computing, Vol. 17, No. 4, pp. 395-416, 2007. Article (CrossRef Link)   DOI
26 J. Shi, and J. Malik, “Normalized cuts and image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, pp. 888-905, 2000. Article (CrossRef Link)   DOI
27 J. Chen, S. Shan, C. He, G. Zhao, M. Pietikäinen, X. Chen, and W. Gao, “WLD: A robust local image descriptor,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 9, pp. 1705-1720, September, 2010. Article (CrossRef Link)   DOI
28 P. Neubert, and P. Protzel, "Compact watershed and preemptive SLIC: On improving trade-offs of superpixel segmentation algorithms," in Proc. of the IEEE International Conference on Pattern Recognition, pp. 996-1001, August 24-28, 2014. Article (CrossRef Link)
29 R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “SLIC superpixels compared to state-of-the-art superpixel methods,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, No. 11, pp. 2274-2282, May, 2012. Article (CrossRef Link)   DOI
30 H. Lu, L. Jiang, and A. Zell, "Long range traversable region detection based on superpixels clustering for mobile robots," in Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems, September 28 - October 2, 2015. Article (CrossRef Link)
31 S. H. Cha, “Comprehensive survey on distance/similarity measures between probability density functions,” International Journal of Mathematical Models & Methods in Appliedences, Vol. 1, No. 4, pp. 300-307, January, 2007. Article (CrossRef Link)
32 J. Wang, and M. F. Cohen, “Image and video matting: A survey,” Foundations and Trends in Computer Graphics and Vision, Vol. 3, No. 2, pp. 97-175, 2007. Article (CrossRef Link)   DOI
33 C. Rother, V. Kolmogorov, and A. Blake, “Grabcut: Interactive foreground extraction using iterated graph cuts,” ACM Transactions on Graphics, Vol. 23, No. 3, pp. 309-314, 2004. Article (CrossRef Link)   DOI
34 D. L. Olson, and D. Delen, Advanced data mining techniques, 1st Edition, Springer, Berlin, 2008. Article (CrossRef Link)