Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
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
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-Time Video Compression: Techniques and Algorithms
Real-Time Video Compression: Techniques and Algorithms
Video-Based Surveillance Systems: Computer Vision and Distributed Processing
Video-Based Surveillance Systems: Computer Vision and Distributed Processing
Introduction to the Special Section on Video Surveillance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Real-time adaptive background segmentation
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Separating non-stationary from stationary scene components in a sequence of real world TV-images
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Texture Dissimilarity Measures for Background Change Detection
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Multi-layer Background Change Detection Based on Spatiotemporal Texture Projections
ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
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We estimate the speed of texture change by measuring the spread of texture vectors in their feature space. This method allows us to robustly detect even very slow moving objects. By learning a normal amount of texture change over time, we are also able to detect increased activities in videos. We illustrate the performance of the proposed techniques on videos from PETS repository and the Temple University Police department.