High Real-Time Multi-Object Tracking Algorithm for Complex Scenarios

In this paper, we propose an enhanced Multi-Object Tracking(MOT) framework based on ByteTrack, achieving dual improvements in efficiency and performance while significantly enhancing robustness in complex scenarios. During the initial matching stage, we integrate an appearance feature matching branch employing a Vmamba backbone network to mitigate occlusion-induced detection failures caused by significant appearance variations. Simultaneously, we introduce a computationally optimized appearance feature extraction method to reduce redundant computational overhead and improve resource utilization. Comprehensive evaluations demonstrate the framework's effectiveness in high-density scenarios, achieving state-of-the-art performance on MOT17 test set with 80.9 MOTA, 79.6 IDF1, and 64.4 HOTA, while maintaining real-time processing at 26.6 FPS. The proposed method also exhibits superior performance on MOT20 benchmark, particularly in addressing severe occlusion challenges and preserving target identity consistency.