Real-Time Face Tracking under Long-Term Full Occlusions

The identified weaknesses of most of state-of-the-art trackers are inability to cope with long-term full occlusions, abrupt motion, detecting and tracking a reappeared target. In this paper, we present a robust real-time single face tracking system with several new key features: semi-automatic target tracking initialization based on a robust face detector, an effective target loss estimation based on a response of a position correlation filter, a candidate image patch selection for reinitialization supported with a short-and long-term memories (STM and LTM). These memories are used for tracking reinitialization during online learning procedure. The STM is used to select an image patch as candidate for re-tracking based on stored position correlation filters (from current frame) in case of short-term full occlusions, while the LTM stores aggregated position correlation filters (online learned) is used to recover the tracker from long-term full occlusions. Validation of the tracking system was performed by evaluation on a subset of videos from Online Tracking Benchmark (OTB) dataset and our own video.