Discovery and Segmentation of Activities in Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection of abnormal behaviors using a mixture of Von Mises distributions
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
A DSP-based system for the detection of vehicles parked in prohibited areas
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Detection of abandoned objects in crowded environments
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
An efficient method for detecting ghost and left objects in surveillance video
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
IEEE Transactions on Image Processing
Neural Network Approach to Background Modeling for Video Object Segmentation
IEEE Transactions on Neural Networks
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Background determination is crucial to visual intelligent surveillance systems. Although several methods have been proposed in the literature, research on this topic is still a paramount objective in the surveillance system community. High performance and low computational cost in a video segmentation model are some of the characteristics of the segmentation model presented in this paper. The model is designed to work with semi-static backgrounds. The segmentation model is based on a SOM like architecture. Weights neuron updates are performed in the fly to provide dynamic background actualization. The model keeps simplicity but it is tolerant to background variations like illumination, shadows, and slow moving background regions. The method was tested in several scenarios, including daytime and night situations, as well as interior and exterior scenarios. Qualitative and quantitative results of the model show high performance for normal backgrounds, and acceptable performance on high dynamic backgrounds, compared with complex models reported in the literature.