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Open AccessArticle

Recognition of Human Activities Using Depth Maps and the Viewpoint Feature Histogram Descriptor

1
Section of Informatization of the Course of Studies, Rzeszow University of Technology, al. Powstancow Warszawy, 12 35-959 Rzeszow, Poland
2
Department of Computer and Control Engineering, Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, W. Pola 2, 35-959 Rzeszow, Poland
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(10), 2940; https://doi.org/10.3390/s20102940
Received: 11 April 2020 / Revised: 8 May 2020 / Accepted: 20 May 2020 / Published: 22 May 2020
(This article belongs to the Section Physical Sensors)
In this paper we propose a way of using depth maps transformed into 3D point clouds to classify human activities. The activities are described as time sequences of feature vectors based on the Viewpoint Feature Histogram descriptor (VFH) computed using the Point Cloud Library. Recognition is performed by two types of classifiers: (i) k-NN nearest neighbors’ classifier with Dynamic Time Warping measure, (ii) bidirectional long short-term memory (BiLSTM) deep learning networks. Reduction of classification time for the k-NN by introducing a two tier model and improvement of BiLSTM-based classification via transfer learning and combining multiple networks by fuzzy integral are discussed. Our classification results obtained on two representative datasets: University of Texas at Dallas Multimodal Human Action Dataset and Mining Software Repositories Action 3D Dataset are comparable or better than the current state of the art. View Full-Text
Keywords: point clouds; VFH descriptor; activity recognition; dynamic time warping; BiLSTM; transfer learning; multiple network fusion point clouds; VFH descriptor; activity recognition; dynamic time warping; BiLSTM; transfer learning; multiple network fusion
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Sidor, K.; Wysocki, M. Recognition of Human Activities Using Depth Maps and the Viewpoint Feature Histogram Descriptor. Sensors 2020, 20, 2940.

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