Object Tracking Algorithm with Fusion of Multi-feature and Channel Awareness

Abstract

In order to solve the problem of drift or overfitting in the tracking process of depth feature description target, an object tracking algorithm combining multiple features and channel perception is proposed. The depth feature of the tracking target is extracted by the pre-training model, the correlation filter is built according to the feature, and the weight coefficient of each channel filter is calculated. According to the weight coefficient, the feature channel generated by the pre-training model is screened. The standard deviation of the retained features is calculated to generate statistical features and they are fused with the original features. The fused features are used to construct related filters and correlation operations are performed to obtain feature response maps to determine the location and scale of the target. Based on the depth feature of the tracking result area, the filter constructed by fusion feature is made sparse online updates. The algorithm in this paper and some current mainstream tracking algorithms are tested on the public datasets OTB100, VOT2015 and VOT2016. Compared with UDT, without affecting the tracking speed, the proposed algorithm has stronger robustness and higher tracking accuracy. The experimental results show that the proposed algorithm shows strong robustness under the challenges of target scale variation, fast motion and background clutters.

Keywords

• |object tracking|depth feature|channel screening|feature fusion|sparse update