A self-adaptive weighted affinity propagation clustering for key frames extraction on human action recognition

• We propose a novel approach for key frames extraction on human action recognition.
• Energy Feature (EF) presents human action effectively and intuitively.
• We propose a Self-adaptive Weighted Affinity Propagation (SWAP) algorithm.

In this paper, we propose a novel approach for key frames extraction on human action recognition from 3D video sequences. To represent human actions, an Energy Feature (EF), combining kinetic energy and potential energy, is extracted from 3D video sequences. A Self-adaptive Weighted Affinity Propagation (SWAP) algorithm is then proposed to extract the key frames. Finally, we employ SVM to recognize human actions on the EFs of selected key frames. The experiments show the information including whole action course can be effectively extracted by our method, and we obtain good recognition performance without losing classification accuracy. Moreover, the recognition speed is greatly improved.

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