Elements of information theory
Elements of information theory
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
New approximations of differential entropy for independent component analysis and projection pursuit
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Special Section on Video Surveillance
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
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Object Tracking Using Adaptive Color Mixture Models
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume I - Volume I
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Incremental PCA or On-Line Visual Learning and Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Candid Covariance-Free Incremental Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Motion Detection Based on Local Variation of Spatiotemporal Texture
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 8 - Volume 08
Real-time adaptive background segmentation
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
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Although a tremendous effort has been made to perform a reliable analysis of images and videos in the past fifty years, the reality is that one cannot rely 100% on the analysis results. With exception of applications in controlled environments (e.g., machine vision application), one has to deal with an open world, which means that content of images may significantly change, and it seems impossible to predict all possible changes. Relying on content-based video analysis may lead to bogus results, since the observed changes may be consequence of unreliable features, and not necessarily of observed events of interest. Our main strategy is to estimate the feature properties when the features are reliable computed, so that any set of features that does not have these properties is detected as being unreliable. This way we do not perform any direct content analysis, but instead perform unsupervised analysis of feature properties that are related to the reliability. The solution pursuit in this paper is to monitor the reliability of the computed features using temporal changes and statistical properties of feature value distributions. Results on benchmark real-life videos demonstrate the capability of the proposed techniques to successfully eliminate problems due to change in light conditions, transition/compression artifacts and unwanted camera motions.