🤖 AI Summary
To address key challenges in multi-object tracking under partial occlusion—namely inaccurate motion estimation, degraded appearance features, and unfair data association—this paper proposes an adaptive occlusion-aware tracking framework. Methodologically: (1) an improved Kalman filter with anomaly motion suppression is designed to enhance motion prediction robustness during occlusion; (2) a pose-keypoint-guided part-based re-identification module is introduced to mitigate appearance mismatch caused by local occlusion; and (3) an occlusion-aware IoU-appearance weighted joint association strategy is formulated to ensure fair and stable data association. Extensive experiments on MOTChallenge and DanceTrack benchmarks demonstrate significant improvements over state-of-the-art methods: IDF1 increases notably, ID switches are substantially reduced, and visualizations confirm markedly enhanced occlusion robustness.
📝 Abstract
Multiple pedestrian tracking is crucial for enhancing safety and efficiency in intelligent transport and autonomous driving systems by predicting movements and enabling adaptive decision-making in dynamic environments. It optimizes traffic flow, facilitates human interaction, and ensures compliance with regulations. However, it faces the challenge of tracking pedestrians in the presence of occlusion. Existing methods overlook effects caused by abnormal detections during partial occlusion. Subsequently, these abnormal detections can lead to inaccurate motion estimation, unreliable appearance features, and unfair association. To address these issues, we propose an adaptive occlusion-aware multiple pedestrian tracker, OccluTrack, to mitigate the effects caused by partial occlusion. Specifically, we first introduce a plug-and-play abnormal motion suppression mechanism into the Kalman Filter to adaptively detect and suppress outlier motions caused by partial occlusion. Second, we develop a pose-guided re-identification (Re-ID) module to extract discriminative part features for partially occluded pedestrians. Last, we develop a new occlusion-aware association method towards fair Intersection over Union (IoU) and appearance embedding distance measurement for occluded pedestrians. Extensive evaluation results demonstrate that our method outperforms state-of-the-art methods on MOTChallenge and DanceTrack datasets. Particularly, the performance improvements on IDF1 and ID Switches, as well as visualized results, demonstrate the effectiveness of our method in multiple pedestrian tracking.