(Received B; revised B; in final form B) Q2The paper is devoted to the problem of estimating the number of people visible in a camera. It uses as features the ratio of foreground pixels in each cell of a rectangular grid. Using the above features and data mining techniques allowed reaching accuracy up to 85% for exact match and up to 95% for plus-minus one estimates for an indoor surveillance environment. Applying median filters to the sequence of estimation results increased the accuracy up to 91% for exact match. The architecture of a real-time people counting estimator is suggested. The results of analysis of experimental data are provided and discussed.

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This paper describes a method for unsupervised classification of events in multi-camera indoors surveillance video. This research is a part of the Multiple Sensor Indoor Surveillance (MSIS) project which uses 32 AXIS-2100 webcams that observe an office environment. The research was inspired by the following practical problem: how automatically classify and visualize a 24 hour long video captured by 32 cameras? Raw data are sequences of JPEG images captured by webcams at the rate 2-6 Hz. The following features are extracted from the image data: foreground pixels' spatial distribution and color histogram. The data are integrated by event by averaging motion and color features and creating a "summary" frame which accumulates all foreground pixels of frames of the event into one image. The self-organizing map (SOM) approach is applied to event data for clustering and visualization. One-level and two-level SOM clustering are used. A tool for browsing results allows exploring units of the SOM maps at different levels of hierarchy, clusters of units and distances between units in 3D space. A special technique has been developed to visualize rare events. The results are presented and discussed.

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