Wide area video surveillance with spatial-temporal constraints

Abstract

We propose a novel method for the problem of target retrieval for wide area video surveillance (WAVS). Different from previous methods, our algorithm does not require environment model construction nor the learning or usage of any transition probabilities. Furthermore, our method can be applied to an arbitrarily large area for surveillance. The essence of our method lies in an optimization problem formulation using only the shortest driving distance between pairs of cameras, and the provision of an algorithm that finds a global optimum to the optimization. Experiments show that our method can achieve 99% precision and > 80% recall on an input of thousands of frames, which far exceeds the performance of the baseline, whilst taking under 10 seconds to run on a 1.67GHz Intel Centrino laptop. This makes it feasibly deployable on handheld devices in a police car.