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Fall
Detection using Modular Neural Networks and Back-projected Optical Flow |
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Authors: Chieh-Ling
Huang, E-Liang Chen, and Pau-Choo
Chung Contact: Chieh-Ling
Huang (kaio@neural.ee.ncku.edu.tw) |
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Project Description |
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This project presents a video-based algorithm for fall detection. The algorithm is based on the back-projected optical flow and modular neural networks. From a video sequence, the moving object is first extracted and the pixels with high variance of the extracted object are determined as feature points. Then the proposed back-projected optical flow is employed to estimate the genuine motion of these feature points. The normalized accumulated values of four directions of the estimated motion vectors of the feature points form a to-be-recognized feature vector. The sequence of feature vectors is fed into a time-delay neural network modular to detect whether a falling event occurs. The outputs of different modules, which have learned different moving direction of the object, are fed into a committee neural network for fall detection.
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Publications |
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1. Chieh-Ling Huang, E-Liang Chen, Pau-Choo Chung, “Fall Detection using Modular Neural Networks and Back-projected Optical Flow”, Neural Information Processing. The 12th International Conference on , Oct 30 - Nov 2 2005 |
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Copyright
c Smart Media & Intelligent
Living Excellence Lab., Department
of Electrical Engineering, National
Cheng Kung University, |
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