BGTracker: Cross-Task Bidirectional Guidance Strategy for Multiple Object Tracking

Abstract

Recent works have shown that the joint-detection-and-embedding (JDE) paradigm has significantly enhanced the performance of multiple object tracking by simultaneously learning detection and re-identification features. These methods always utilize a weight-shared backbone network and two non-interactive branches for different tasks. This non-interactive multi-task learning strategy cannot make full use of geometric and semantic information between detection and re-identification tasks. And in the JDE paradigm, there exists a feature misalignment between detection and re-identification due to their different optimization directions. In this paper, BGTracker is proposed as a novel online tracking framework with a cross-task bidirectional guidance strategy between detection and re-identification. Firstly, we propose a Channel-based Decoupling module and Cross-direction Transformer to alleviate feature misalignment, which can obtain task-aligned embeddings and discriminative representations at the feature level. Then, we propose the bidirectional guidance strategy to link the two tasks by the prediction map's statistical information. In this strategy, two designed feature transformations are employed to utilize the advantages of each task for complementing each other at the task level. Finally, extensive experiments demonstrate that the proposed BGTracker outperforms various existing methods on the MOTChallenge benchmarks.