Stochastic model-based heuristics for fast field of view loss recovery in urban traffic management through networks of video cameras

This paper proposes two new heuristic methods for real-time distributed traffic monitoring through video cameras. The goal is to minimize the field-of-view (FOV) loss of the cameras due to dynamic obstacles while considering the timing constraints of the application. The methods utilize different cost functions to select the cameras used in FOV loss recovery. The cost functions are based on a new stochastic model for traffic monitoring, including the dynamics of mobile obstacles, unreliable communication, and resolution and timing constraints. The first cost function addresses deterministic situations by capturing the tradeoff between the quality of recovery and the imposed timing constraints. The second cost function captures stochastic aspects, such as a camera being obstructed by obstacles or experiencing data losses due to unreliable communication. Experiments show that the two methods offer reliable FOV loss recovery for a large variety of conditions. The methods are fast and scale well with the number of monitored cars and cameras. The average FOV loss recovery of the deterministic heuristic is 52%, but the resulting coverage remains close to 100% most of the time, whereas without the recovery scheme, the coverage drops to about 60% about half the time. For time-constrained unreliable communication, the stochastic heuristic offers coverage that is only about 15% less than if communication is unrestricted.