Video Object Segmentation Based on Superpixel Trajectories
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.
KeywordsSuperpixel trajectory Object segmentation Affinity
The authors would like to thank Egyptian Ministry of Higher Education (MoHE) and Egypt-Japan University of Science and Technology (E-JUST) for their support.
- 2.Papazoglou, A., Ferrari, V.: Fast object segmentation in unconstrained video. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1777–1784 (2013)Google Scholar
- 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)Google Scholar
- 7.Faktor, A., Irani, M.: Video segmentation by non-local consensus voting. In: BMVC (2014)Google Scholar
- 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)Google Scholar