Multi-tracklet Tracking for Generic Targets with Adaptive Detection Clustering

• URL: http://arxiv.org/abs/2508.05172v1
• Date: Thu, 07 Aug 2025 09:05:27 GMT
• Title: Multi-tracklet Tracking for Generic Targets with Adaptive Detection Clustering
• Authors: Zewei Wu, Longhao Wang, Cui Wang, César Teixeira, Wei Ke, Zhang Xiong,
• Abstract summary: This article proposes a tracklet tracker called Multi-Tracklet Tracking (MTT) that integrates flexible tracklet generation into a multi-tracklet association framework.<n> experiments on the benchmark for generic multiple object tracking demonstrate the competitiveness of the proposed framework.
• Score: 8.637143090635396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tracking specific targets, such as pedestrians and vehicles, has been the focus of recent vision-based multitarget tracking studies. However, in some real-world scenarios, unseen categories often challenge existing methods due to low-confidence detections, weak motion and appearance constraints, and long-term occlusions. To address these issues, this article proposes a tracklet-enhanced tracker called Multi-Tracklet Tracking (MTT) that integrates flexible tracklet generation into a multi-tracklet association framework. This framework first adaptively clusters the detection results according to their short-term spatio-temporal correlation into robust tracklets and then estimates the best tracklet partitions using multiple clues, such as location and appearance over time to mitigate error propagation in long-term association. Finally, extensive experiments on the benchmark for generic multiple object tracking demonstrate the competitiveness of the proposed framework.

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