Dynamic Network Flow Optimization for Task Scheduling in PTZ Camera Surveillance Systems

TL;DR

This paper introduces a real-time, scalable camera scheduling system using Kalman filters and network flow optimization to improve surveillance coverage and responsiveness in dynamic environments.

Contribution

It combines motion prediction with network flow models and group tracking to optimize PTZ camera scheduling, a novel integration for surveillance systems.

Findings

Improves coverage and reduces wait times.

Minimizes missed critical events.

Enhances scalability and adaptability.

Abstract

This paper presents a novel approach for optimizing the scheduling and control of Pan-Tilt-Zoom (PTZ) cameras in dynamic surveillance environments. The proposed method integrates Kalman filters for motion prediction with a dynamic network flow model to enhance real-time video capture efficiency. By assigning Kalman filters to tracked objects, the system predicts future locations, enabling precise scheduling of camera tasks. This prediction-driven approach is formulated as a network flow optimization, ensuring scalability and adaptability to various surveillance scenarios. To further reduce redundant monitoring, we also incorporate group-tracking nodes, allowing multiple objects to be captured within a single camera focus when appropriate. In addition, a value-based system is introduced to prioritize camera actions, focusing on the timely capture of critical events. By adjusting the decay rates of these values over time, the system ensures prompt responses to tasks with imminent deadlines. Extensive simulations demonstrate that this approach improves coverage, reduces average wait times, and minimizes missed events compared to traditional master-slave camera systems. Overall, our method significantly enhances the efficiency, scalability, and effectiveness of surveillance systems, particularly in dynamic and crowded environments.