Empirical evaluation of the exclusion approach to estimating camera overlap

Abstract

Making intelligent decisions on the basis of the video captured by a large network of surveillance cameras requires the ability to identify overlap between their fields of view. Without this information it is impossible to perform even simple analysis, such as distinguishing between repeated behaviours and multiple views of the same behaviour. Large-scale intelligent video surveillance thus requires a means of understanding the relationships between the fields of view of the cameras involved. The exclusion approach is the only method currently capable of performing online estimation of camera overlap for networks of more than 50 cameras, with a version of the algorithm applicable to 1000 camera networks having been published. Empirical evaluation of every such algorithm is critical to assessing its performance, and essential if comparisons between methods are to be made. This paper presents a method by which such an empirical evaluation may be carried out, and makes publicly available the data (including ground truth) on which it based in order that competing methods might be compared equally. Precision vs recall curves are reported for a series of experiments comparing the results of exclusion to ground truth. These results demonstrate the strengths and limitations of the exclusion-based estimation process, but show that the performance of the method exceeds the requirements of surveillance applications.