Occlusion-Aware SORT: Observing Occlusion for Robust Multi-Object Tracking

2026-03-06

AI Summary

This work addresses the challenge of partial occlusion in 2D multi-object tracking, which often leads to ambiguity in association costs and degrades trajectory analysis and counting accuracy. To this end, the authors propose OA-SORT, a plug-and-play, training-free occlusion-aware framework that explicitly models occlusion states and dynamically adjusts association costs and motion estimation. OA-SORT introduces three novel components: an Occlusion-Aware Module (OAM), Occlusion-Aware Offset (OAO), and Bias-Aware Momentum (BAM). By incorporating Gaussian maps to suppress background interference, the framework seamlessly integrates into existing trackers. Evaluated on DanceTrack, OA-SORT achieves 63.1% HOTA and 64.2% IDF1. When integrated with four mainstream trackers, it yields average improvements of 2.08% in HOTA and 3.05% in IDF1.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Security and Privacy: Tracking, profiling, and countermeasures against them
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Multi-object tracking (MOT) involves analyzing object trajectories and counting the number of objects in video sequences. However, 2D MOT faces challenges due to positional cost confusion arising from partial occlusion. To address this issue, we present the novel Occlusion-Aware SORT (OA-SORT) framework, a plug-and-play and training-free framework that includes the Occlusion-Aware Module (OAM), the Occlusion-Aware Offset (OAO), and the Bias-Aware Momentum (BAM). Specifically, OAM analyzes the occlusion status of objects, where a Gaussian Map (GM) is introduced to reduce background influence. In contrast, OAO and BAM leverage the OAM-described occlusion status to mitigate cost confusion and suppress estimation instability. Comprehensive evaluations on the DanceTrack, SportsMOT, and MOT17 datasets demonstrate the importance of occlusion handling in MOT. On the DanceTrack test set, OA-SORT achieves 63.1% and 64.2% in HOTA and IDF1, respectively. Furthermore, integrating the Occlusion-Aware framework into the four additional trackers improves HOTA and IDF1 by an average of 2.08% and 3.05%, demonstrating the reusability of the occlusion awareness.

Problem

• multi-object tracking
• occlusion
• cost confusion
• robustness
• 2D MOT

Innovation

• Occlusion-Aware
• Multi-Object Tracking
• Plug-and-Play
• Gaussian Map
• Cost Confusion Mitigation