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REAL-SORT: RElation-aware for real-time multiple object tracking
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AI
DOI：10.1016/j.knosys.2026.115373.png)
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Abstract
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Recent advancements in multi-object tracking (MOT) have accelerated progress in autonomous driving and human-computer interaction. Tracking-by-detection approaches remain dominant due to their computational efficiency and streamlined architectures. However, this paradigm faces two critical challenges that trade off tracking accuracy with real-time efficiency: (i) spatial cues often exhibit inconsistent reliability across diverse scenarios, limiting their effectiveness; and (ii) false associations across consecutive frames frequently cause tracking failures, undermining long-term robustness. To address these issues, we propose Relation-Aware Simple Online and Real-time Tracker (REAL-SORT), which effectively leverages both spatial and temporal relationships. Regarding spatial relations, we introduce two association strategies that incorporate occlusion-aware cross-scenario feature extraction and relative-position-based matching. On the temporal side, an ID Recovery Module (IRM) exploits multi-frame information to estimate ID lost probabilities, enabling robust trajectory recovery. Extensive experiments on DanceTrack, MOT17, and MOT20 benchmarks demonstrate that our method outperforms existing state-of-the-art trackers across HOTA, IDF1 and AssA metrics, particularly excelling in challenging scenarios. Furthermore, REAL-SORT exhibits strong generalizability, consistently improving performance when integrated into various leading tracking frameworks.
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