AC-SORT: Adaptive Combination of Appearance and Motion Information for Multi-Object Tracking

The tracking-by-detection paradigm typically in-volves two primary steps: object detection and data association. In recent years, researchers have begun to introduce appearance features into the association stage to reduce ID switches and improve trajectory reconstruction capability. Although this approach has made significant progress, it still encounters various challenges, such as the susceptibility of appearance features to environmental noise and the inappropriate combination of motion and appearance information. To address above problems, we use the advanced motion-based OC-SORT as the baseline for improvement in this work. Firstly, we integrate appearance features into the association stage and use an adaptive appearance updating strategy to ensure the robustness of these features. Secondly, we adaptively adjust the weighting of motion and appearance features during the association stage, according to the number of consecutive frames in which the trajectory fails to match any detections. This adaptive adjustment aids in recovering objects that were lost due to long-term occlusion. Finally, we implement a camera motion compensation module, which enhances the accuracy of capturing object motion information correcting image displacement in real time, thereby further optimizing tracking performance. We validate the improved algorithm using multiple public datasets, and the experimental results show that our method significantly improves the stability of tracking.