Robust Visual Tracking with Hierarchical Deep Features Weighted Fusion

To solve the problem of low robustness of trackers under significant appearance changes in complex background, a novel moving target tracking method based on hierarchical deep features weighted fusion and correlation filter is proposed. Firstly, multi-layer features are extracted by a deep model pre-trained on massive object recognition datasets. The linearly separable features of Relu3-1, Relu4-1 and Relu5-4 layers from VGG-Net-19 are especially suitable for target tracking. Then, correlation filters over hierarchical convolutional features are learned to generate their correlation response maps. Finally, a novel approach of weight adjustment is presented to fuse response maps. The maximum value of the final response map is just the location of the target. Extensive experiments on the object tracking benchmark datasets demonstrate the high robustness and recognition precision compared with several state-of-the-art trackers under the different conditions.