Boosting One-Stage Multi Object Tracking with Attention Learning

One-stage multi-object tracking methods have achieved promising results by showing their great balance between accuracy and speed. However, the internal differences and relationships between detection and re-identification (re-ID) lead to worse performance. In this work, we propose a one-stage multi-object tracking method with attention boosting, namely AeMOT, which can effectively improve the collaboration and performance in detection and re-ID. Specifically, a discriminability enhancement module is designed to enhance the discriminative feature representations for detection and tracking, and an identity preserving module is properly designed to preserve the semantic alignment of id-embedding and improve the adaptiveness of object matching with scale variation for re-ID association. Experimental results on challenging benchmarks including MOT17 and MOT20 demonstrate that our proposed method achieves leading performance and outperforms state-of-the-art trackers.