一次性融合时空信息，实现高效多摄像头车辆跟踪
Spatial-Temporal Multi-Cuts for Online Multiple-Camera Vehicle Tracking
- 设计统一图结构，同时进行时空聚类，避免分步处理
- 在CityFlow和Synthehicle上分别提升IDF1超14%和25%
- 无需训练或额外标注，适合实时部署的智能交通系统
准确的在线多摄像头车辆跟踪对智能交通系统、自动驾驶和智慧城市应用至关重要。与单摄像头多目标跟踪类似，该问题通常被建模为基于检测的图问题。现有在线方法多采用两阶段流程：先时序聚类再空间聚类，或反之，计算开销大且易积累误差。本文提出一种新型图表示，支持时空聚类的一体化处理：新检测结果同时与已有轨迹簇在时空上连接。通过保留每个簇中所有检测的稀疏外观与位置线索，可基于最强证据比较轨迹簇。最终通过简单的多割分配（multicut）在线获得完整轨迹。本方法无需目标场景训练、无需预先提取单摄像头轨迹，也无需额外标注。显著优于当前在线方法：在CityFlow数据集上IDF1提升超过14%，在Synthehicle数据集上提升超过25%。代码已公开。
原文摘要 · Abstract (English)
Accurate online multiple-camera vehicle tracking is essential for intelligent transportation systems, autonomous driving, and smart city applications. Like single-camera multiple-object tracking, it is commonly formulated as a graph problem of tracking-by-detection. Within this framework, existing online methods usually consist of two-stage procedures that cluster temporally first, then spatially, or vice versa. This is computationally expensive and prone to error accumulation. We introduce a graph representation that allows spatial-temporal clustering in a single, combined step: New detections are spatially and temporally connected with existing clusters. By keeping sparse appearance and positional cues of all detections in a cluster, our method can compare clusters based on the strongest available evidence. The final tracks are obtained online using a simple multicut assignment procedure. Our method does not require any training on the target scene, pre-extraction of single-camera tracks, or additional annotations. Notably, we outperform the online state-of-the-art on the CityFlow dataset in terms of IDF1 by more than 14%, and on the Synthehicle dataset by more than 25%, respectively. The code is publicly available.
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