Video Object Segmentation Based on Superpixel Trajectories

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• 关键词：Superpixel trajectory ; Object segmentation ; Affinity
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9730
• 期：1
• 页码：191-197
• 全文大小：2,266 KB
• 参考文献：1.Zhou, X., Yang, C., Weichuan, Y.: Moving object detection by detecting contiguous outliers in the low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 597–610 (2013)CrossRef
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  4.Elqursh, A., Elgammal, A.: Online moving camera background subtraction. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 228–241. Springer, Heidelberg (2012)CrossRef
  5.Chen, L., Shen, J., Wang, W., Ni, B.: Video object segmentation via dense trajectories. IEEE Trans. Multimed. 17(12), 2225–2234 (2015)CrossRef
  6.Chang, J., Wei, D., Fisher, J.: A video representation using temporal superpixels. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2051–2058 (2013)
  7.Faktor, A., Irani, M.: Video segmentation by non-local consensus voting. In: BMVC (2014)
  8.Fragkiadaki, K., Zhang, G., Shi, J.: Video segmentation by tracing discontinuities in a trajectory embedding. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1846–1853 (2012)
  
• 作者单位：Mohamed A. Abdelwahab (15) (16)
  Moataz M. Abdelwahab (15)
  Hideaki Uchiyama (16)
  Atsushi Shimada (16)
  Rin-ichiro Taniguchi (16)
  
  15. Egypt Japan University of Science and Technology, Alexandria, Egypt
  16. Kyushu University, Fukuoka, Japan
  
• 丛书名：Image Analysis and Recognition
• ISBN：978-3-319-41501-7
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9730

文摘

In this paper, a video object segmentation method utilizing the motion of superpixel centroids is proposed. Our method achieves the same advantages of methods based on clustering point trajectories, furthermore obtaining dense clustering labels from sparse ones becomes very easy. Simply for each superpixel the label of its centroid is propagated to all its entire pixels. In addition to the motion of superpixel centroids, histogram of oriented optical flow, HOOF, extracted from superpixels is used as a second feature. After segmenting each object, we distinguish between foreground objects and the background utilizing the obtained clustering results.