Adaptive Cross-Camera Video Analytics on Edge Device

With the rise of edge devices, video analytics has become a key application in edge computing. Single-camera systems are limited by their Field of View (FoV), making them inadequate for complex environments such as traffic intersections. In this paper, we propose Adaptive Cross-Camera Video Analytics (ACCVA), a novel system for intelligent traffic monitoring. ACCVA introduces a novel camera selection algorithm that adaptively chooses the best camera based on vehicle location and historical data, as well as an adaptive retention algorithm to prevent occlusion and recover lost objects. ACCVA establishes dynamic segmentation of camera regions and cross-camera correlation, managing real-time video inference and result sharing. Implemented on the NVIDIA Jetson Orin NX and evaluated with a real-world traffic surveillance dataset, ACCVA significantly reduces end-to-end latency and enhances accuracy compared to existing state-of-the-art systems. ACCVA excels in complex scenarios by balancing low latency and high accuracy, providing an effective solution for intelligent traffic monitoring.