Introduction - If you have any usage issues, please Google them yourself
The object is defined by its location and extent in a single frame. In every frame that follows, the task is to determine the object s location and extent or indicate that the object is not present. We propose a novel tracking framework (TLD) that explicitly decomposes the long-term tracking task into tracking, learning and detection. The tracker follows the object frame to frame. The detector localizes all appearances that have been observed so far and corrects the tracker if necessary. The learning estimates detector s errors and updates it to avoid these errors in the future. We study how to identify detector s errors and learn them. We develop a novel learning method (P-N learning) which estimates the errors by a pair of experts : (i) P-expert estimates missed detections, and (ii) N-expert estimates false alarms. The learning process is modeled as a discrete dynamical system and the conditions under which the learning guarantees improvement are found. We describe our real-time