Abstract:

A novel object tracking algorithm fused with the visual dictionary and online multiple instance learning tracking (MILTrack) is proposed to solve the problem of tracking failure detection and scale changes in MILTrack algorithm. It regards the visual dictionary and MILTrack as detector and tracker respectively. Mutual feedback technology is employed for improving the tracking performance. The dictionary is constructed and updated by the training sample obtained from the tracker, while the detector make decision whether the object is lost or tracked. If we lost the object, a detection is implemented in a larger area. Otherwise, Ransac algorithm is utilized to obtain the scaling factors of the target, under which the tracker is updated. In order to improve the accuracy of the loss decision of the target, we propose a local random sampling of histogram similarity measure technique. The idea of local division and Noisy-NR model is employed for the measurement of similarity between the histograms of candidate sample and training target samples. The results shows that our algorithm makes the MILTrack algorithm adaptively adjust the scale of the target, and the detection of tracking failure is possible. The stability of tracking is improved.