Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments
Descriptive temporal template features for visual motion recognition
Pattern Recognition Letters
Real-time multiview recognition of human gestures by distributed image processing
Journal on Image and Video Processing - Special issue on fast and robust methods for multiple-view vision
SOMM: Self organizing Markov map for gesture recognition
Pattern Recognition Letters
Probabilistic video-based gesture recognition using self-organizing feature maps
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
A macro-observation scheme for abnormal event detection in daily-life video sequences
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
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An approach to recognise and segment 9 elementary gestures from a video input is proposed and it can be applied to continuous sign recognition. An isolated gesture is recognised by first converting a portion of video into a motion gradient orientation image and then classifying it into one of the 9 gestures by a sparse Bayesian classifier. The portion of video used is decided by using a sampling technique based on CONDENSATION framework. By doing so, gestures can be segmented from the video in a probabilistic manner. Experiments show that the proposed method can achieve accuracy around 90% in both isolated and continuous gesture recognition without using special equipment such as glove devices and the system can run in real-time.