Abstract
Effective video monitoring systems require optimization of camera and road network coverage, to exploit fully the hardware and software solutions in smart city traffic applications. Monitoring requirements have grown increasingly diverse as scenes are becoming increasingly complex, thereby transforming the camera and road network coverage optimization issue into a nonlinear, high-dimension, and multi-objective problem. Previous research on this topic however, has focused on a single, specific optimization objective, which may result in invalid optimization results in actual applications. To extend this research, we propose a multi-objective scheduling optimization algorithm for a camera network that addresses the problem of directional road network coverage. In this solution, we incorporate an expanding parameter of main optical axes into particle swarm optimization algorithm. Our new strategy divides the range of main optical axes of all the cameras to control the scheduling number, achieving collaborative optimization of multiple objectives. In a simulated camera and road network, an experiment was designed for evaluating the effectiveness of the proposed method, comparing the distribution of optimization results with the global and local optimal solutions of the true value. A second experiment compared the distribution, performance and running time of the optimization results with different values of expanding parameter of main optical axes. A third experiment compared the performance of the optimization solutions with different values of camera parameters. The results showed that the proposed method can adapt to user application preference, and is effective and robust to schedule and allocate monitoring resources in different scenarios.
Highlights
Monitoring cameras are sensors used to observe both static scenes and moving objects in the Internet of Things and Smart cities, given their real time, continuity, and reproducibility
It demonstrates the ability of the proposed method to balance multiple optimization objectives and obtain favorable optimization results
To address the optimization problem of camera and road network coverage, we propose a multi-objective scheduling optimization algorithm
Summary
Monitoring cameras are sensors used to observe both static scenes and moving objects in the Internet of Things and Smart cities, given their real time, continuity, and reproducibility. Cameras are typically deployed on the intersections of road network to monitor moving vehicles and pedestrians whose direction and trajectories are greatly constrained by the road network. These systems are not quite effective, as their low scheduling flexibility and cooperativity, which generates several challenges about how to improve coverage quality of available monitoring resources faced with various application demands.
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