Video Object Segmentation Via Dense Trajectories

In this paper, we propose a novel approach to segment moving object in video by utilizing improved point trajectories . First, point trajectories are densely sampled from video and tracked through optical flow, which provides information of long-term temporal interactions among objects in the video sequence . Second, a novel affinity measurement method considering both global and local information of point trajectories is proposed to cluster trajectories into groups. Finally, we propose a new graph-based segmentation method which adopts both local and global motion information encoded by the tracked dense point trajectories. The proposed approach achieves good performance on trajectory clustering, and it also obtains accurate video object segmentation results on both the Moseg dataset and our new dataset containing more challenging videos.