A multi-objective scheduling optimization algorithm of a camera network for directional road network coverage

Area of Science:

• Computer Science
• Traffic Engineering
• Optimization Theory

Background:

• Smart city traffic monitoring demands sophisticated camera and road network coverage optimization.
• Increasing scene complexity transforms coverage optimization into a high-dimension, multi-objective problem.
• Existing methods often focus on single objectives, limiting real-world applicability.

Purpose of the Study:

• To develop a multi-objective scheduling optimization algorithm for camera networks.
• To address directional road network coverage challenges in smart city traffic applications.
• To improve the collaborative optimization of multiple objectives for monitoring systems.

Main Methods:

• Incorporation of an expanding parameter of main optical axes into a particle swarm optimization algorithm.
• Division of main optical axes range to control scheduling and achieve multi-objective optimization.
• Experimental evaluation using simulated camera and road networks to assess effectiveness and robustness.

Main Results:

• The proposed method effectively schedules and allocates monitoring resources, adapting to user preferences.
• Experimental results demonstrate the algorithm's effectiveness and robustness across various scenarios.
• Performance analysis confirmed the method's ability to handle diverse camera parameters and optimization objectives.

Conclusions:

• The multi-objective algorithm offers a robust solution for optimizing camera network coverage in smart cities.
• This approach enhances the efficiency and adaptability of traffic monitoring systems.
• The method provides a significant advancement over single-objective optimization techniques.