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This paper presents a home-care system for recognizing six kinds of daily activities (including walk, jogging, in-place actions like standing, sitting and squat, stand-to-sit, stand-to-squat, and fall) from videos by a multi-SVM classifier with decision tree structure. The system first detects human blobs by a non-parameter background subtraction method, then extracts shape and motion features from...
Even great efforts have been made for decades, the recognition of human activities is still an unmature technology that attracted plenty of people in computer vision. In this paper, a system framework is presented to recognize multiple kinds of activities from videos by an SVM multi-class classifier with a binary tree architecture. The framework is composed of three functionally cascaded modules:...
Fall detection system for intelligent home care for elderly people is presented in this paper. The system includes human blob detection by non-parameter background substruction method, feature extraction from two minimum bounding boxes, and fall detection by a cascaded multi-SVM classifier. Besides falling down, other daily activities such as walk, jogging, sitting down, squatting down and immobility...
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