Self-organizing maps
PicSOM—content-based image retrieval with self-organizing maps
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Kohonen Maps
Fusion of Multiple Tracking Algorithms for Robust People Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Multi-Camera Multi-Person Tracking for EasyLiving
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Support for effective use of multiple video streams in security
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
Using the self organizing map for clustering of text documents
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Video genre classification using weighted kernel logistic regression
Advances in Multimedia - Special issue on Multimedia Applications for Smart Device in Ubiquitous Environments
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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.