Pedestrian Scene Coverage Control Using Perceptive Quality-Based Virtual Potential Field

Abstract

Comprehensive observation of target area with pedestrians through pan-tilt-zoom (PTZ) camera networks is crucial in various surveillance applications. However, the dynamic configuration of PTZ cameras increases the difficulty of coordinating multiple cameras to monitor large-scale scenes. Since coverage control in PTZ camera networks has been proven to be an NP-hard problem, many studies have adopted virtual potential field (VPF) algorithms to efficiently obtain approximate solutions. The VPF methods treat camera viewpoints as charged particles. Through repulsive forces between these particles, PTZ camera networks expand scene coverage and reduce overlap between camera fields of view (FoVs). However, VPF-based methods cannot leverage the scene layout and target priority information, failing to cover pedestrians and other critical areas. In this work, we introduce a unified perception quality measure framework that quantifies surveillance importance for scenes, cameras, and pedestrians. Building on this framework, we design a coverage control scheme using a perceptive quality-based virtual potential field. This scheme models target regions and pedestrian priorities as virtual gravitational and attractive forces. It maximizes coverage of key regions, minimizes camera overlap, and supports high-resolution monitoring and tracking of pedestrians. Extensive experiments show that our approach outperforms state-of-the-art methods, achieving superior scene and pedestrian coverage performance.