Fundamentals of speech recognition
Fundamentals of speech recognition
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Human motion analysis: a review
Computer Vision and Image Understanding
ACM Computing Surveys (CSUR)
Special Section on Video Surveillance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Periodic Motion Detection, Analysis, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Analysis of Human Gestures
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Video Annotation for Content-based Retrieval using Human Behavior Analysis and Domain Knowledge
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Incremental Nonlinear Dimensionality Reduction by Manifold Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Computer Vision and Image Understanding
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning atomic human actions using variable-length Markov models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Human motion recognition using Isomap and dynamic time warping
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Inferring 3D body pose from silhouettes using activity manifold learning
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Motion Flow-Based Video Retrieval
IEEE Transactions on Multimedia
Video-Based Human Movement Analysis and Its Application to Surveillance Systems
IEEE Transactions on Multimedia
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Visual analysis of human behavior has attracted a great deal of attention in the field of computer vision because of the wide variety of potential applications. Human behavior can be segmented into atomic actions, each of which indicates a single, basic movement. To reduce human intervention in the analysis of human behavior, unsupervised learning may be more suitable than supervised learning. However, the complex nature of human behavior analysis makes unsupervised learning a challenging task. In this paper, we propose a framework for the unsupervised analysis of human behavior based on manifold learning. First, a pairwise human posture distance matrix is derived from a training action sequence. Then, the isometric feature mapping (Isomap) algorithm is applied to construct a low-dimensional structure from the distance matrix. Consequently, the training action sequence is mapped into a manifold trajectory in the Isomap space. To identify the break points between the trajectories of any two successive atomic actions, we represent the manifold trajectory in the Isomap space as a time series of low-dimensional points. A temporal segmentation technique is then applied to segment the time series into sub series, each of which corresponds to an atomic action. Next, the dynamic time warping (DTW) approach is used to cluster atomic action sequences. Finally, we use the clustering results to learn and classify atomic actions according to the nearest neighbor rule. If the distance between the input sequence and the nearest mean sequence is greater than a given threshold, it is regarded as an unknown atomic action. Experiments conducted on real data demonstrate the effectiveness of the proposed method.