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Surveillance is an essential part of any security operation, with cameras playing an important role. Often these cameras are controlled by a single individual, but this can lead to inefficient surveillance that leaves areas uncovered and misses targets.We propose a constraint satisfaction problem (CSP) solution to surveilling an area that maximizes the area seen in a region of interest (ROI), minimizes the time individual cells in the ROI goes unseen, and prioritizes targets for tracking. Cells in the ROI are weighted based on the time since they were last seen and whether or not a target is predicted to be in the cell. A virtual environment with cameras and targets is surveilled based on various parameters affecting camera actions. Several metrics are used to measure the performance of each set of parameters, including cell time unseen, average linear uncovered length (ALUL), and time a target is seen with results compared to another surveillance algorithm. The CSP was able to view all cells while greatly increasing the target tracking ability of rarely viewed targets at the cost of slightly fewer viewing steps for the more often viewed targets.

Prior to his current position at Vanderbilt University, he was a Member of Technical Staff at Lucent Bell Labs. He is a Senior Member of both the IEEE and ACM.His research interests are in solving distributed systems challenges for real-time and embedded systems through effective software engineering principles and algorithm development. He is applying these expertise to develop an effective, cloud-based and ubiquitous infrastructure for scalable, collaborative STEM education.