针对未知目标追踪难题，提出多轨迹关联新方法。
Multi-tracklet Tracking for Generic Targets with Adaptive Detection Clustering
- 基于时空相关性自适应聚类检测结果生成鲁棒轨迹段。
- 融合位置与外观信息，优化长时轨迹分割，减少误差传播。
- 适用于低置信度、遮挡严重等复杂场景，适合通用目标追踪任务。
针对行人、车辆等特定目标的视觉多目标追踪研究已取得显著进展。然而，在真实场景中，由于检测置信度低、运动与外观约束弱、长期遮挡等问题，未见类别目标常导致现有方法失效。为此，本文提出一种增强型轨迹追踪框架——多轨迹追踪（MTT），将灵活的轨迹段生成融入多轨迹关联机制。该框架首先根据短时时空相关性自适应聚类检测结果，生成鲁棒轨迹段；随后利用位置、外观等多线索估计最优轨迹段划分，有效缓解长时关联中的误差传播问题。在通用多目标追踪基准上的大量实验表明，所提方法具有较强的竞争力。
原文摘要 · Abstract (English)
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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