Affiliations: [a] School of Communication and Information Engineering, Shanghai University, Shanghai, China | [b] Safety and Emergency Laboratory, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China | [c] College of Information Engineering, Shanghai Maritime University, Shanghai, China
Correspondence:
[*]
Corresponding author: Bo Zhang, Safety and Emergency Laboratory, Shanghai Advanced Research Institute, Chinese Academy of Sciences, No. 99 Haike Road, Zhangjiang Hi-Tech Park, Pudong Shanghai 201210, China. E-mail:zhangbo@sari.ac.cn
Abstract: In this paper, we propose an algorithm for human activity
recognition based on Gaussian Process Classifier (GPC). A hierarchical
strategy is firstly applied to classify dynamic and static behaviors. Then,
in each layer, three kinds of classification approaches are validated and
evaluated for promoting recognition accuracy. Moreover, discriminative
analysis method is invoked to cast high dimension features into lower
dimensional space where classes are easily separated. Extensive experiments
have been conducted and three vital points are observed: Firstly, GPC
achieves comparable classification accuracy with other classifiers under the
same experimental condition. Secondly, in case of less training samples, GPC
outperforms the prominent Support Vector Machine (SVM) classifier. Thirdly,
unlike SVM, GPC is more robust to the high dimensional features.
Furthermore, we successfully implement the presented recognition algorithm
into our hardware platform and achieve 99.75% accuracy on average in
dealing with four sample activities.
Keywords: Human activity recognition, Gaussian Process classifier, discriminative analysis, support vector machine
