Description of Human Activity Using Behavioral Primitives
Human activity is a subject of tracking and recognition in various aspects including: public security, health lifestyle or home monitoring of elderly. A multimodal surveillance system is proposed to recognize the action and to assess the similarity of temporal behavioral patterns. The system uses sensor networks, automatic measurement module and decision making procedure to recognize the potentially dangerous events. It uses behavioral primitives (as positions, movements or vital signs) and their temporal relations to determine the current activity of the subject.
The idea of decomposition of human behavior description is developed throughout this paper. In principles it originates from the signal theory and assumes that any behavioral pattern can be represented by a linear combination of independent elementary actions. These actions should be carefully selected to provide a minimal redundancy and ease the measurement and robust recognition in real systems.
KeywordsSensor Network Gaussian Mixture Model Wearable Sensor CIELab Color Space Decomposition Base
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