Fusion of Tracking Techniques to Enhance Adaptive Real-time Tracking of Arbitrary Objects

Peter Poschmann, Patrik Huber, Matthias Rätsch, Joseph Kittler, Hans-Joachim Böhme
2014 Procedia Computer Science  
In visual adaptive tracking, the tracker adapts to the target, background, and conditions of the image sequence. Each update introduces some error, so the tracker might drift away from the target over time. To increase the robustness against the drifting problem, we present three ideas on top of a particle filter framework: An optical-flow-based motion estimation, a learning strategy for preventing bad updates while staying adaptive, and a sliding window detector for failure detection and
more » ... detection and finding the best training examples. We experimentally evaluate the ideas using the BoBoT dataset a . The code of our tracker is available online b .
doi:10.1016/j.procs.2014.11.025 fatcat:gzgir3txuvdy7c3lsqvjfxhu6q