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Current surveillance systems consist of large numbers of cameras. The video feeds from cameras are automatically processed for threat detection, which is a computationally in tensive task. In order to meet the real-time requirements of surveillance, we need to distribute the video processing over multiple computers. Generally the cameras are statically as signed to the processors; 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 the targets in its view. In future, this uneven distribution of workload will become more critical as the sensing infrastructures are being deployed on the cloud. In this work, we model the camera workload as a function of the number of targets, and use that to dynamically assign video feeds to the processors. Experimental results show that the proposed model successfully captures the variability of the workload, and that dynamic workload assignment provides better results than a static assignment.