Adaptive Workload Equalization in Multi-Camera Surveillance Systems
Surveillance and monitoring systems generally employ a large number of cameras to capture people's activities in the environment. These activities are analyzed by hosts (human operators and/or computers) for threat detection. Threat detection is a target centric task in which the behavior of each target is analyzed separately, which requires a significant amount of human attention and is a computationally intensive task for automatic analysis. In order to meet the real-time requirements of surveillance, it is necessary to distribute the video processing load over multiple hosts. In general, cameras are statically assigned to the hosts; we show that this is not a desirable solution as the workload for a particular camera may vary over time depending on the number of targets in its view. In the future, this uneven distribution of workload will become more critical as the sensing infrastructures are being deployed on the cloud. In this paper, we model the camera workload as a function of the number of targets, and use that to dynamically assign video feeds to the hosts. Experimental results show that the proposed model successfully captures the variability of the workload, and that the dynamic workload assignment provides better results than a static assignment.