A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Advances in view-invariant human motion analysis: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Robust sequence alignment for actor-object interaction recognition: Discovering actor-object states
Computer Vision and Image Understanding
Hierarchical visual event pattern mining and its applications
Data Mining and Knowledge Discovery
A suspicious behaviour detection using a context space model for smart surveillance systems
Computer Vision and Image Understanding
Survey on classifying human actions through visual sensors
Artificial Intelligence Review
A review on vision techniques applied to Human Behaviour Analysis for Ambient-Assisted Living
Expert Systems with Applications: An International Journal
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This paper proposes a new approach for recognition of task-oriented actions based on stochastic context-free grammar (SCFG). Our attention puts on actions in the Japanese tea ceremony, where the action can be described by context-free grammar. Our aim is to recognize the action in the tea services. Existing SCFG approach consists of generating symbolic string, parsing it and recognition. The symbolic string often includes uncertainty. Therefore, the parsing process needs to recover the errors at the entry process. This paper proposes a segmentation method errorless as much as possible to segment an action into a string of finer actions. This method, based on an acceleration of the body motion, can produce the fine action corresponding to a terminal symbol with little error. After translating the sequence of fine actions into a set of symbolic strings, SCFG-based parsing of this set leaves small number of ones to be derived. Among the remaining strings, Bayesian classifier answers the action name with a maximum posterior probability. Giving one SCFG rule the multiple probabilities, one SCFG can recognize multiple actions.