CIVS: Communication-Aware Industrial Video Surveillance With Edge-End Collaboration

Multi-object tracking (MOT) is crucial for edge-enabled industrial video surveillance. Many video streams are delivered to the edge via limited, dynamic communication channels to be processed efficiently for accurate, on-time surveillance responses. However, time-varying communication conditions and constrained edge computational resources challenge surveillance accuracy and latency. This paper proposes an edge-based industrial video surveillance framework with bandwidth adaptation and edge-end collaboration that operates under limited communication conditions. Besides, a communication-aware industrial video surveillance with edge-end collaboration method (CIVS) is devised to improve surveillance accuracy and latency performance across different bandwidth environments. Furthermore, an NP-hard integer non-linear problem is formulated to maximize the accuracy while minimizing the latency of MOT by configuring the matches between an industrial video network and an edge computing network, which is solved by a branch and bound algorithm. Simulations show CIVS achieves 0.5892s latency and 68.9% multi-object tracking accuracy, outperforming other state-of-the-art.