Temporal Structure Models for Event Recognition

In many areas of visual surveillance, the observed activity follows re-occurring patterns. This paper demonstrates how such patterns can be exploited to improve the detection rate of independent event detectors. We present a temporal model based on pairwise correlations between event timings, which efficiently exploits limited training data. This is combined with the response from potentially heterogeneous independent event detectors to improve the robustness of detections over extended sequences. We demonstrate the efficacy of our system with rigorous testing on a large real-world dataset of aircraft servicing operations. We describe the implementation of a binary classifier based on local histograms of optical flow which is used as the independent event detector in our experiments.