Target Drift Discriminative Network Based on Dual-template Siamese Structure in Long-term Tracking
In long-term visual tracking, most of the target loss discriminative methods require artificially determined thresholds, and the selection of optimal thresholds is usually difficult, resulting in weak generalization ability of long-term tracking algorithms. A target drift Discriminative Network (DNet) that does not require artificially selected thresholds is proposed. The network adopts Siamese structure and uses both static and dynamic templates to determine whether the tracking results are lost or not. Among them, the introduction of dynamic templates effectively improves the algorithm's ability to adapt to changes in target appearance. In order to train the proposed target drift discriminative network, a sample-rich dataset is established. To verify the effectiveness of the proposed network, a complete long-term tracking algorithm is constructed in this paper by combining this network with the base tracker and the re-detection module. It is tested on classical visual tracking datasets such as UAV20L, LaSOT, VOT2018-LT and VOT2020-LT. The experimental results show that compared with the base tracker, the tracking accuracy and success rate are improved by 10.4％ and 7.5％ on UAV20L dataset, respectively.
万方数据
维普
