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.
@article{arxiv.2508.05172,
title = {Multi-tracklet Tracking for Generic Targets with Adaptive Detection Clustering},
author = {Zewei Wu and Longhao Wang and Cui Wang and César Teixeira and Wei Ke and Zhang Xiong},
journal= {arXiv preprint arXiv:2508.05172},
year = {2025}
}