State-of-the-art on spatio-temporal information-based video retrieval
Pattern Recognition
Unsupervised Pedestrian Re-identification for Loitering Detection
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Learning atomic human actions using variable-length Markov models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
A hybrid moving object detection method for aerial images
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Human behavior classification by analyzing periodic motions
Frontiers of Computer Science in China
Skin colour segmentation based 2D and 3D human pose modelling using Discrete Wavelet Transform
Pattern Recognition and Image Analysis
Gait-based action recognition via accelerated minimum incremental coding length classifier
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
A novel shadow-assistant human fall detection scheme using a cascade of SVM classifiers
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Human action segmentation and classification based on the Isomap algorithm
Multimedia Tools and Applications
Human action recognition using silhouette histogram
ACSC '11 Proceedings of the Thirty-Fourth Australasian Computer Science Conference - Volume 113
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This paper presents a novel posture classification system that analyzes human movements directly from video sequences. In the system, each sequence of movements is converted into a posture sequence. To better characterize a posture in a sequence, we triangulate it into triangular meshes, from which we extract two features: the skeleton feature and the centroid context feature. The first feature is used as a coarse representation of the subject, while the second is used to derive a finer description. We adopt a depth-first search (dfs) scheme to extract the skeletal features of a posture from the triangulation result. The proposed skeleton feature extraction scheme is more robust and efficient than conventional silhouette-based approaches. The skeletal features extracted in the first stage are used to extract the centroid context feature, which is a finer representation that can characterize the shape of a whole body or body parts. The two descriptors working together make human movement analysis a very efficient and accurate process because they generate a set of key postures from a movement sequence. The ordered key posture sequence is represented by a symbol string. Matching two arbitrary action sequences then becomes a symbol string matching problem. Our experiment results demonstrate that the proposed method is a robust, accurate, and powerful tool for human movement analysis.