Improving Object Tracking with Voting from False Positive Detections

Context provides additional information in detection and tracking and several works proposed online trained trackers that make use of the context. However, the context is usually considered during tracking as items with motion patterns significantly correlated with the target. We propose a new approach that exploits context in tracking-by-detection and makes use of persistent false positive detections. True detection as well as repeated false positives act as pointers to the location of the target. This is implemented with a generalised Hough voting and incorporated into a state-of-the art online learning framework. The proposed method presents good performance in both speed and accuracy and it improves the current state of the art results in a challenging benchmark.