Information Bottleneck-based relevant knowledge representation in large-scale video surveillance systems

Extraction and representation of relevant information from large-scale surveillance systems constitute fundamental processes for allowing automatic interpretation of complex scenes. In particular, when the amount of information increases (i.e., due to a larger number of monitored areas), attention focusing techniques are needed to highlight most relevant parts within the overall acquired data. When wide area surveillance systems are considered, one of the major problems in event detections is the reconstruction of the scene as a whole, from spatially limited observations. In this paper, a novel representation technique for sparse information, based on information theory, is presented. Self Organizing Maps (SOMs) have been used for classifying and correlating observed sparse data time series. By means of Information Bottleneck theory, it is possible to determine the optimal data representation in the SOM-space as a tradeoff between the signal reconstruction capabilities and the original data statistical similarities preservation. Proposed experiments show how the so called information bottleneck-based SOM selection for knowledge modelling, can be applied to the field of crowd monitoring for people density map estimation and event detection. Results are presented on synthetic and real video sequences.