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. 1

Detection of unusual objects amongst a highly textured background is a difficult problem, especially when the texture is manifest in the temporal dimension as well. Outdoor scenes involving waving trees or moving water are examples of such a scenario, but are nevertheless frequently encountered in real world vision applications. By defining a simple but rotationally sensitive Local Binary Pattern (LBP) operator and applying it in a probabilistic sense we present a compact but useful feature for tackling moving textures. But as we demonstrate, this alone is not sufficient for good segmentation in difficult circumstances. Cooccurrence of different features in a pixel’s local neighbourhood provides a powerful mechanism for boosting the reliability of the foreground/background decision task. By using the conditional probabilities yielded by pairwise cooccurrence of 4-connected pixels, and casting the problem as one of Combinatorial Optimization, our results show that useful segmentation is possible from challenging dynamic backgrounds. 1

Motivated by the demand for an effective background model, robust to non-stationary environmental changes in outdoor scenes, we present a technique using Combinatorial Optimization to extract near-optimal background estimates from blocks of temporally localized frames. Using an existing graph cut technique in conjunction with subspace analysis, we demonstrate a novel background model exhibiting results superior to those achievable with the latter technique alone, and especially suitable for background modelling in outdoor situations where variable lighting conditions prevail. 1

A block-based dynamic background modelling technique featuring incremental update is presented, with the aim of increasing the sensitivity of background detection by considering the local connectivity of pixels. An accumulative model is maintained on-line by a heuristic algorithm to build up a statistical representation of a dynamic scene from a fixed camera view for foreground-background segmentation. The model is compared with a block-based technique employing cooccurrence [M. Seki et al., June 2003], and shown to be favourable in terms of both performance and conceptual simplicity. Using predominantly simple integer operations, the algorithm is highly amenable to direct implementation in hardware.