Dynamic Camera Reconfiguration with Reinforcement Learning and Stochastic Methods for Crowd Surveillance.

Area of Science:

• Computer Science
• Robotics
• Artificial Intelligence

Background:

• Traditional fixed camera surveillance systems have limitations in coverage, resolution, and analytical performance.
• Smart camera networks with active devices like pan-tilt-zoom (PTZ) and UAV-based cameras offer adaptability.
• Dynamic reconfiguration is needed to overcome limitations of static surveillance infrastructure.

Purpose of the Study:

• To propose a novel decentralized approach for smart camera network reconfiguration.
• To enable cameras to dynamically adapt their parameters and positions for optimized scene coverage.
• To balance global scene coverage with high-resolution monitoring of crowded areas.

Main Methods:

• Development of two decentralized camera reconfiguration policies: greedy and reinforcement learning.
• Implementation of local control mechanisms for cameras to adjust neighborhood states.
• Dynamic adjustment of camera positions and PTZ parameters based on real-time scene analysis.
• Simulation environment utilizing fixed, PTZ, and UAV-based cameras.

Main Results:

• The decentralized approach allows cameras to autonomously optimize their configuration.
• The system effectively balances overall scene coverage with detailed monitoring of crowded zones.
• Both greedy and reinforcement learning policies demonstrated successful dynamic network adaptation.
• Evaluated performance in a simulated environment with diverse camera types.

Conclusions:

• Decentralized camera network reconfiguration is a viable strategy for adaptive crowd surveillance.
• The proposed methods enhance surveillance capabilities by optimizing coverage and resolution dynamically.
• Smart camera networks can effectively adapt to changing public space dynamics for improved safety and security.