MVSparse: Distributed Cooperative Multi-camera Multi-target Tracking on the Edge

Tracking people in multi-camera surveillance systems is challenging due to disparate perspectives, large volumes of data, and high computation demands. This paper presents a distributed cooperative pipeline for pedestrian tracking that exploits the spatial and temporal redundancy within and across the video feeds from multiple synchronized cameras. It consists of three key components: 1) a lightweight policy network trained online in a self-supervised manner on each camera, 2) a sparse backbone processing unit purpose-built for parallel processing of selected regions of all cameras, and 3 an online clustering algorithm for object association. Utilizing online distributed reinforcement learning, the fully end-to-end trainable pipeline can accelerate any tracking-by-detection method by reducing detection costs across multiple perspectives. MVSparse has been evaluated using two multi-camera multi-target pedestrian tracking datasets, WildTrack and MultiviewX. It reduces the amount of processed regions by up to 52% and 39% with only moderate degradation of 1% and 0.1% in tracking accuracy on the two datasets, respectively. On a real-world testbed comprising four NVIDIA Jetson TX2 and a GPU server, MVSparse accelerates the end-to-end process and reduces the communication overheads by 1.88 and $1.60 X$ with only 2.27% and 3.17% degradation in tracking accuracy on the two datasets, respectively