Detic-Track: Robust Detection and Tracking of Objects in Video

Hannes Fassold

Abstract: The automatic detection and tracking of objects in a video is crucial for many video understanding tasks. We propose a novel deep learning based algorithm for object detection and tracking, which is able to detect more than 1,000 object classes and tracks them robustly, even for challenging content. The robustness of the tracking is due to the usage of optical flow information. Additionally, we utilize only the part of the bounding box corresponding to the object shape for the tracking.