CDTS: Collaborative Detection, Tracking, and Segmentation for Online Multiple Object Segmentation in Videos

Yeong Jun Koh, Chang-Su Kim

Abstract: A novel online algorithm to segment multiple objects in a video sequence is proposed in this work. We develop the collaborative detection, tracking, and segmentation (CDTS) technique to extract multiple segment tracks accurately. First, we jointly use object detector and tracker to generate multiple bounding box tracks for objects. Second, we transform each bounding box into a pixel-wise segment, by employing the alternate shrinking and expansion (ASE) segmentation. Third, we refine the segment tracks, by detecting object disappearance and reappearance cases and merging overlapping segment tracks. Experimental results show that the proposed algorithm significantly surpasses the state-of-the-art conventional algorithms on benchmark datasets.