SegTrack: A novel tracking system with improved object segmentation

Abstract

Most tracking methods depend on a rectangle or an ellipse mask to segment and track objects. Typically, using a larger or smaller mask will lead to loss of tracked objects. In this paper, we propose an object tracking system (SegTrack) that deals with partial and full occlusions by employing improved segmentation methods. Our improved mixture of Gaussians segments foreground objects from the background and solves stop-then-move and move-then-stop problems. Then, the KLT tracker tracks objects in consecutive frames and detects partial and full occlusions. In partial occlusion, a novel silhouette segmentation algorithm evolves the silhouettes of occluded objects by matching the location and appearance of occluded objects between successive frames. In full occlusion, one or more feature vectors for each tracked object are used to re-identify the object after reappearing. Our experimental results show that SegTrack provides more accurate and robust tracking when compared to other state-of-the-art trackers.