A motion classification model with improved robustness through deformation code integration

• Published in: Neural computing & applications 2019-12, Vol.31 (12), p.8519-8532
• Main Authors: Xia, Lei, Lv, Jiancheng, Liu, Dongbo
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Deformation; Human motion; Image Processing and Computer Vision; Monte Carlo simulation; Original Article; Probability and Statistics in Computer Science; Robustness; Time series
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-018-3681-0

During data acquisition, samples in a time series may contain noise, such as inconsistent data ranges, inconsistent data, and incomplete data. Therefore, the classification model requires improved robustness to correctly classify the sequence of human motion. This paper presents a classification model with improved robustness performance based on the factored gated restricted Boltzmann machine to effectively overcome the various aforementioned data problems. The proposed model acquires the deformation code of each action first and integrates the deformation codes together to be an integrated deformation code of the entire sequence. Then, the model determines the classification from the integrated deformation code. This approach mainly focuses on the deformation relations among action samples in the extraction sequence, and it ignores the data expression in the sequence samples. Experiments show that the proposed model performs better than state-of-the-art approaches in terms of the robustness of time series classification with noise.