OPEN ACCESS
Human actions recognition generally uses geometric or statistical characteristics as training data. The geometric characteristics of the image and waveform display can be described by Pulse coupled neural network (PCNN). Experiential model decomposition (EMD) algorithm can be used for the feature extraction of waveforms. Therefore, we propose a motion feature description algorithm combined with PCNN and EMD. The experimental results show that using the method of PCNN-EMD-based feature recognition can obtain a high accuracy rate, while using the fusion feature of KPCA has a higher recognition rate.
human action recognition, EMD, gabor, PCNN, KPCA
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