Toward robust activity recognition: Hierarchical classifier based on Gaussian Process
Xiaomei Wang, Bo Zhang, Fuping Zhang, Guowei Teng, Zuolei Sun, Jianming Wei · Intelligent Data Analysis · 2016
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 promot ing 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.