Background modelling and performance metrics for visual surveillance
This work deals with the problems of performance evaluation and background modelling for the detection of moving objects in outdoor video surveillance datasets. Such datasets are typically affected by considerable background variations caused by global and partial illumination variations, gradual and sudden lighting condition changes, and non-stationary backgrounds. The large variation of backgrounds in typical outdoor video sequences requires highly adaptable and robust models able to represent the background at any time instance with sufficient accuracy. Furthermore, in real life applications it is often required to detect possible contaminations of the scene in real time or when new observations become available. A novel adaptive multi-modal algorithm for on-line background modelling is proposed. The proposed algorithm applies the principles of the Gaussian Mixture Model, previously used to model the grey-level (or colour) variations of individual pixels, to the modelling of illumination variations in image regions. The image observations are represented in the eigen-space, where the dimensionality of the data is significantly reduced using the method of the principal components analysis. The projections of image regions in the reduced eigen-space are clustered using K-means into clusters (or modes) of similar backgrounds and are modelled as multivariate Gaussian distributions. Such an approach allows the model to adapts to the changes in the dataset in a timely manner. This work proposed modifications to a previously published method for incremental update of the uni-modal eigne-models. The modifications are twofold. First, the incremental update is performed on the individual modes of the multi-modal model, and second, the mechanism for adding new dimensions is adapted to handle problems typical for outdoor video surveillance scenes with a wide range of illumination changes. Finally, a novel, objective, comparative, object-based methodology for performance evaluation of object detection is also developed. the proposed methodology is concerned with the evaluation of object detection in the context of the end-user defined quality of performance in complex video surveillance applications.