Gradient-Guided Object Tracking Algorithm with Channel Selection

Abstract

In object tracking task, the target object to be tracked is arbitrary, and there may be similar distractor around the target, which often leads to the target features extracted by the pre-trained network not fully applicable to the tracked target. To solve the above-mentioned issues, the gradient-guided object tracking algorithm is proposed in the Siamese tracking framework. Firstly, the pre-trained network is used to extract the features of object. To eliminate the interference of similar objects, the switch-penalty loss function is used to impose penalty operation on similar objects in the background. Secondly, in the feature channel selection stage, the most expressive feature channels are selected according to the gradient information of back propagation in loss function. Finally, in the part of cross correlation between template branch and search branch, the accurate target position is obtained by using multi-channel cross correlation of the weighted response score map. The proposed algorithm is compared with the mainstream algorithms on OTB and VOT public datasets. Experimental results show that the proposed algorithm has good anti-background interference ability and robustness. The algorithm achieves the performance of the mainstream tracking algorithms in the main tracking indicators.

Keywords

• |object tracking|gradient-guided network|penalty function|multi-channel cross correlation