Learning Online Policies for Person Tracking in Multi-View Environments
Keivan Nalaie Rong Zheng
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.