A Lightweight Object Tracking Method Based on Transformer
Ziwen Sun, Chuandong Yang, Chong Ling · 2023
Transformer is widely used in the field of object tracking by virtue of its powerful global modeling capability, but the existing Transformer-based object tracking method is slow due to the large number of parameters, which limits the use of the algorithm on mobile smart devices, in order to reduce the number of parameters to further improve the tracking speed, a lightweight object tracking based on Transformer is proposed. Transformer-based lightweight object tracking method is proposed. First, in the feature extraction part, the first three layers of Swin-Transformer network are used to extract deep features from the input initial template, dynamic template, and search area, respectively. Second, in order to make full use of the initial template information, the extracted features are feature-enhanced with the help of the cross-attention module, and the features after the enhancement of the initial template and the dynamic template are sparsified by the sparsification module. Then, the fused features are fed into the simplified encoder and decoder modules for feature fusion. Finally, the output features go through a bounding box prediction header to predict the tracked object and a quality scoring header to determine whether to update the dynamic template. The algorithm is experimented on the LaSOT dataset, and the tracking success rate reaches 70.8% and the tracking speed reaches 78.7 FPS. The tracking speed is improved by significantly reducing the number of parameters while guaranteeing the tracking accuracy, which proves the effectiveness of the lightweight tracking method.