A Long-Memory Pedestrian Target Tracking Algorithm Incorporating Spatiotemporal Trajectory Feature Enhancement Model

Multi-target tracking is a complex problem in computer vision, involving occlusion, target trajectory disappearance, etc. In response to these questions, we first propose a trajectory coordinate bi-directional sensing attention module (BasicCaModule). This module enables the network to be more focused on essential areas. Secondly, we propose the Feature Confusion Edge Enhancement (FCEE) module, which enhances the confusion region of adjacent feature edges. After detecting the network output feature map, we propose the adaptive feature separation header (FDH) module. This module can activate essential features adaptively to reduce the number of target identity switches. Finally, appropriate hyperparameters are set and optimized for the ByteTrack tracking algorithm, resolving fragment tracking loss and long memory matching problems. After a large amount of experimental data, our approach is highly competitive with the existing state-of-the-art algorithms. Moreover, it can also keep online tracking while maintaining high tracking accuracy. The code is publicly available at: https://github.com/CccJ23/Spatio-temporal-trajectory-Tracking