Visual tracking based on multi-feature fusion

Abstract

Object feature representation is one of the most important links in visual tracking. Traditional single feature are difficult to accurately describe the appearance characteristics of the target, and it is difficult to accurately track the target for large appearance changes. In view of the above problems, we use the convolution feature and color histogram feature to train the deep and shallow correlation filters respectively to jointly represent the object features, and propose a dynamic weight strategy to adaptive combine the two features. The background information around the target is introduced by introducing the context aware framework as the training samples to jointly train the filter. The combination weights of deep and shallow layers are calculated through the dynamic weight strategy, so as to realize the adaptive combination of the two features. The proposed algorithm is tested on OTB2015 data set. The results show that the algorithm can improve the accuracy and success rate of tracking