Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
Video-Based Surveillance Systems: Computer Vision and Distributed Processing
Video-Based Surveillance Systems: Computer Vision and Distributed Processing
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
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
A Two-Stage Template Approach to Person Detection in Thermal Imagery
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Real-time adaptive background segmentation
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Reverse kNN search in arbitrary dimensionality
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Incremental connectivity-based outlier factor algorithm
VoCS'08 Proceedings of the 2008 international conference on Visions of Computer Science: BCS International Academic Conference
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In this paper, we describe a technique for detection of moving objects in RGB and infra-red (IR) videos. The technique is based on novel incremental connectivity-based outlier factor (IncCOF). The main idea of the proposed approach is to detect moving blocks as outliers---objects dissimilar to objects in their vicinity--within a properly defined feature space. As the feature space, we use representation of videos by spatial-temporal blocks combined with principal component analysis for dimensionality reduction. Experimental evaluation of the proposed approach on a variety of test videos, including PETS repository, demonstrates its applicability and robustness on the choice of parameters.