Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Retrieval of Similar Time Sequences Under Time Warping
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
An Adaptive Appearance Model Approach for Model-based Articulated Object Tracking
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Model based human motion tracking using probability evolutionary algorithm
Pattern Recognition Letters
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Hierarchical querying scheme of human motions for smart home environment
Engineering Applications of Artificial Intelligence
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Smart spaces represent an emerging new paradigm that encompasses diverse active research areas such as ubiquitous, grid and cloud computing. Hence, there are a wide variety of interesting issues and applications for smart spaces, and surveillance is one issue that has long received much attention. In many cases, human motion is one of the most important clues used in assessing a situation for surveillance purposes. In this paper, we propose a new human abnormality detection scheme for surveillance purposes. More specifically, we first present a motion sequence matching algorithm called Dynamic View Warping to represent specific motion characteristics. Secondly, we propose a matching speed-up technique called Dynamic Group Warping that establishes boundaries in Dynamic View Warping. Thirdly, we propose an indexing scheme for motion sequences and present K-NN search algorithm to efficiently and effectively find similar motion sequences. Our extensive experiments show that our proposed methods achieve outstanding performance.