Exploiting Periodicity in Recurrent Scenes

There is considerable interest in techniques capable of identifying anomalies and unusual events in busy outdoor scenes, e.g. road junctions. Many approaches achieve this by exploiting deviations in spatial appearance from some expected norm accumulated by a model over time. In this work we show that much can be gained from explicitly modelling temporal aspects of scene activity in detail. We characterize a scene by identifying the fundamental period of change on a spatial block-by-block basis by estimating autocovariance of self-similarity. As our model, we introduce a spatio-temporal grid of histograms built corresponding to some chosen feature. This model is then used to identify objects found in unexpected spatial and temporal locations in subsequent test data. Employing a Phase-Locked Loop technique, we describe a method of ensuring that the spatio-temporal model maintains synchronization with learned scene activity in spite of short-term breakdown in the reliability of acquired data, and long-term change of the mean fundamental period. Results indicate our model to be capable of discrimination between behavioural aspects of cars at a typical road junction sufficiently well to provide useful warnings of adverse activity in real time.