Multi-layered Decomposition of Recurrent Scenes

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Abstract

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 in detail. Specifically, many traffic junctions are regulated by lights controlled by a timing device of considerable precision, and it is in these situations that we advocate a model which learns periodic spatio-temporal patterns with a view to highlighting anomalous events such as broken-down vehicles, traffic accidents, or pedestrians jay-walking. More specifically, by estimating autocovariance of self-similarity, used previously in the context gait recognition, we characterize a scene by identifying a global fundamental period. As our model, we introduce a spatio-temporal grid of histograms built in accordance with some chosen feature. This model is then used to classify objects found in subsequent test data. In particular we demonstrate the effect of such characterization experimentally by monitoring the bounding box aspect ratio and optical flow field of objects detected on a road traffic junction, enabling our model to discriminate between people and cars sufficiently well to provide useful warnings of adverse behaviour in real time.