Improved C-COT based on feature channels confidence for visual tracking

摘要

In the field of visual tracking, the methods of Discriminative Correlation Filters (DCF) have showed excellent performance, which rely heavily on the choice of feature descriptors. The Continuous Convolution Operator Tracker (C-COT) is a novel correlation filter to track the target position in the continuous domain, which achieved significant effects. However, as for various visual scenes, different feature descriptors are suitable to different environments. If each feature channel is given the same confidence during the tracking phase, it would limit the performance of some good features. To address this problem, this paper proposes an improved C-COT algorithm that can adaptively perform feature channel weighting. The Average Peak Correlation Energy (APCE) is used to evaluate the corresponding response map of each feature channel, guiding the target appearance model to give different weights to different features. Then, we can obtain the final weighted feature response map whose peak value is applied to locate the target. In addition, the C-COT updates the appearance model rigorously in every frame, which may lead to over-fitting and increase computional complexity. Therefore, in order to reduce the redundancy of the online training sample and avoid similar background interference, we adopts the method of Peak Side Lobe Ratio (PSLR) to update the model. We perform comprehensive experiments on OTB50 and OTB100. The results show that the improved tracker achieves better accuracy, especially in some specific video scenes. In addition, speed has also improved.