Video object detection by model-based tracking

This paper presents an approach to detect moving and static objects occurring in a video by a novel model-based tracking. The method exploits the spatial and motion coherence of objects across image frames that results from the known bounded shape distortion and object's velocity between two consecutive frames. The interframe transformation space is thus reduced to a reasonable small space of only tens or hundreds of possible states. Considering each state as a class, the object tracking process to locate objects across frames can be implemented by a classification framework, comprising a Hough-voting framework and a class-specific implicit video object model. Given a frame of the input video clip, we divide each frame of a test video clip into multiple patches which search similar model patches in the learnt implicit video object model to locate the target objects from the frames. Patch similarity is defined with respect to appearance and motion features of patches. Results show that the proposed method gives good performance on several publicly available datasets in terms of detection accuracy.