ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Fusion of movement specific human identification experts
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
Human identification from human movements
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Anthropocentric video analysis for film and games postproduction
Proceedings of the 11th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing on International Conference on Computer Systems and Technologies
Multi-view human movement recognition based on fuzzy distances and linear discriminant analysis
Computer Vision and Image Understanding
Dynamic action recognition based on dynemes and Extreme Learning Machine
Pattern Recognition Letters
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In this paper, a novel method for continuous human movement recognition based on fuzzy vector quantization (FVQ) and linear discriminant analysis (LDA) is proposed. We regard a movement as a unique combination of basic movement patterns, the so-called dynemes. The proposed algorithm combines FVQ and LDA to discover the most discriminative dynemes as well as represent and discriminate the different human movements in terms of these dynemes. This method allows for simple Mahalanobis or cosine distance comparison of not aligned human movements, taking into account implicitly time shifts and internal speed variations, and, thus, aiding the design of a real-time continuous human movement recognition algorithm. The effectiveness and robustness of this method is shown by experimental results on a standard dataset with videos captured under real conditions, and on a new video dataset created using motion capture data.