Human Action Recognition Method Based on Multi-feature Multi-classifier Fusion
Journal Title: 河南科技大学学报（自然科学版） - Year 2018, Vol 39, Issue 6
Abstract
A human behavior recognition method based on multi-feature and multi-classifier fusion was proposed. For the three-dimensional skeleton motion sequence extracted by the Kinect sensor, the human body motion was described by using the relative geometric features of the body part, the relative position characteristics of the joint points and the absolute position characteristics of the joint points. The support vector machine and the random forest classifier were used as member classifiers to train and test the three kinds of action features respectively. The classifier fusion algorithm was used to make fusion decision for the classification results and achieve the final classification. The verification was performed on the existing human motion dataset. The experimental results show that the method can achieve 95% recognition rate.
Authors and Affiliations
Qingfeng CHEN, Shimin FENG, Enjie DING
Keywords
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