Human motion sequence recognition based on correlation feature selection and multilayer perceptron

Yongjun Gao; Mei Wang; Yunlei Yu; Chen Zhang

doi:10.1117/12.2581258

10 November 2020 Human motion sequence recognition based on correlation feature selection and multilayer perceptron

Yongjun Gao, Mei Wang, Yunlei Yu, Chen Zhang

Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 115841D (2020) https://doi.org/10.1117/12.2581258
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China

Abstract

Aiming at the problems of large dimension and low recognition efficiency of human action sequence recognition, a recognition algorithm based on correlation feature selection and multi-layer perceptron is proposed. Use Kinect to obtain continuous images of human movements. By extracting key frames of human movements and calculating human joint angles, key features of human movements are extracted. Through the correlation feature selection algorithm, the most effective feature subset is selected from a group of features to reduce the dimension of feature space and reduce the calculation amount of classification recognition. Finally, the multi-layer perceptron is used as the classifier, and the BP algorithm is used to adjust the network weights to realize the classification and recognition of human action sequences. Through the classification and recognition experiments of human action sequences, it is proved that the recognition algorithm based on correlation feature selection and multi-layer perceptron has a high recognition rate. The recognition rate of the algorithm proposed in this paper is as high as 96.667%.

Citation Download Citation

Yongjun Gao, Mei Wang, Yunlei Yu, and Chen Zhang "Human motion sequence recognition based on correlation feature selection and multilayer perceptron", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 115841D (10 November 2020); https://doi.org/10.1117/12.2581258

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