In this paper, we introduce MVSparse, a novel and efficient framework for cooperative multi-person tracking across multiple synchronized cameras. The MVSparse system is comprised of a carefully orchestrated pipeline, combining edge server-based models with distributed lightweight Reinforcement Learning (RL) agents operating on individual cameras. These RL agents intelligently select informative blocks within each frame based on historical camera data and detection outcomes from neighboring cameras, significantly reducing computational load and communication overhead. The edge server aggregates multiple camera views to perform detection tasks and provides feedback to the individual agents. By projecting inputs from various perspectives onto a common ground plane and applying deep detection models, MVSparse optimally leverages temporal and spatial redundancy in multi-view videos. Notably, our contributions include an empirical analysis of multi-camera pedestrian tracking datasets, the development of a multi-camera, multi-person detection pipeline, and the implementation of MVSparse, yielding impressive results on both open datasets and real-world scenarios. Experimentally, MVSparse accelerates overall inference time by 1.88X and 1.60X compared to a baseline approach while only marginally compromising tracking accuracy by 2.27% and 3.17%, respectively, showcasing its promising potential for efficient multi-camera tracking applications.

介绍了一种名为MVSparse的合作式多人跟踪框架，它由一个精心设计的管道组成，将基于边缘服务器的模型与在各个摄像头上操作的分布式轻量级强化学习代理相结合，通过选择每个帧内基于历史相机数据和相邻摄像机的检测结果的信息块来显著减少计算负载和通信开销，通过将各个视角的输入投影到共同的地面平面上并应用深度检测模型，MVSparse 在多视角视频中优化地利用了时间和空间冗余；实验证明，与基准方法相比，MVSparse将整体推理时间加速了1.88倍和1.60倍，同时仅对跟踪准确性造成了2.27%和3.17%的轻微损失，展示了其在高效多摄像头跟踪应用中的潜力。

多视角环境下的在线人员追踪学习策略