CALTracker: Cross-Task Association Learning for Multiple Object Tracking

Multiple object tracking has recently achieved excellent performance based on the joint optimization of detection and re-identification tasks. However, joint optimization normally homogenizes the features in the detection and re-identification tasks, which weakens the representation of each task's inherent geometric and semantic information. Additionally, the stability of the tracking trajectory will be impacted by the feature misalignment of the associated information between different tasks. In this letter, we propose a Cross-task Association Learning Tracker (CALTracker) to trade off the inherent and associated information. We first design a Triplet Shrinkage Decoupling (TSD) module to ensure the independence of sub-task features in the optimization process, thereby minimizing the optimization conflicts caused by homogeneous features. Secondly, to improve the consistent representation of the associated information between subtasks, a Double Attention Cross-task Learning (DACL) strategy is designed to achieve cross-task feature alignment and mutual gain. Finally, extensive experimental results on MOT17 and MOT20 demonstrate the effectiveness of the proposed method over the state-of-the-art performance.