  Employing one or more additional classifiers to break the self-learning loop
in tracing-by-detection has gained considerable attention. Most of such
trackers merely utilize the redundancy to address the accumulating label error
in the tracking loop, and suffer from high computational complexity as well as
tracking challenges that may interrupt all classifiers (e.g. temporal
occlusions). We propose the active co-tracking framework, in which the main
classifier of the tracker labels samples of the video sequence, and only
consults auxiliary classifier when it is uncertain. Based on the source of the
uncertainty and the differences of two classifiers (e.g. accuracy, speed,
update frequency, etc.), different policies should be taken to exchange the
information between two classifiers. Here, we introduce a reinforcement
learning approach to find the appropriate policy by considering the state of
the tracker in a specific sequence. The proposed method yields promising
results in comparison to the best tracking-by-detection approaches.
