无需密集标注，通过时间图上的随机游走实现自监督多目标追踪
Walker: Self-supervised Multiple Object Tracking by Walking on Temporal Appearance Graphs
- 构建时序外观图，用多正例对比学习捕捉实例相似性
- 在仅需原标注1/400的情况下，性能媲美主流方法
- 适合标注稀缺场景下的多目标追踪应用
当前最先进的多目标追踪（MOT）方法依赖大量人工标注，需为每段视频中所有帧提供边界框和实例ID。为此，我们提出Walker，首个基于稀疏边界框标注且无追踪标签的自监督追踪器。首先，构建准稠密的时间物体外观图，并设计新型多正例对比目标，优化图上的随机游走以学习实例相似性；其次，提出算法强制图中不同实例间保持互斥连接性，优化拓扑结构以适应追踪任务。推理时，基于运动约束的双向游走最大似然转移状态将检测结果关联到轨迹。Walker是首个在MOT17、DanceTrack和BDD100K上达到竞争力性能的自监督追踪器，即使标注量减少高达400倍，仍优于此前自监督方法。
原文摘要 · Abstract (English)
The supervision of state-of-the-art multiple object tracking (MOT) methods requires enormous annotation efforts to provide bounding boxes for all frames of all videos, and instance IDs to associate them through time. To this end, we introduce Walker, the first self-supervised tracker that learns from videos with sparse bounding box annotations, and no tracking labels. First, we design a quasi-dense temporal object appearance graph, and propose a novel multi-positive contrastive objective to optimize random walks on the graph and learn instance similarities. Then, we introduce an algorithm to enforce mutually-exclusive connective properties across instances in the graph, optimizing the learned topology for MOT. At inference time, we propose to associate detected instances to tracklets based on the max-likelihood transition state under motion-constrained bi-directional walks. Walker is the first self-supervised tracker to achieve competitive performance on MOT17, DanceTrack, and BDD100K. Remarkably, our proposal outperforms the previous self-supervised trackers even when drastically reducing the annotation requirements by up to 400x.
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