Performance of optical flow techniques
International Journal of Computer Vision
Image difference threshold strategies and shadow detection
BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data- and Model-Driven Gaze Control for an Active-Vision System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Attention Mechanisms
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
A self-organizing approach to detection of moving patterns for real-time applications
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Self-organizing Neural System for Background and Foreground Modeling
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
A self-organizing approach to detection of moving patterns for real-time applications
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Visual navigation of mobile robot using optical flow and visual potential field
RobVis'08 Proceedings of the 2nd international conference on Robot vision
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Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications. Aside from the intrinsic usefulness of being able to segment video streams into moving and background components, detecting moving objects provides a focus of attention for recognition, classification, and activity analysis, making these later steps more efficient. We propose an approach based on self organization through artificial neural networks, widely applied in human image processing systems and more generally in cognitive science. The proposed model allows to capture structural background variation due to periodic-like motion over a long period of time under limited memory. Our method can handle scenes containing moving backgrounds or illumination variations, and it achieves robust detection for different types of videos taken with stationary cameras. We compared our method with other modeling techniques. Experimental results, both in terms of detection accuracy and in terms of processing speed, are presented for color video sequences which represent typical situations critical for video surveillance systems.