Abstract
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.
Highlights
One of the most important tasks of human-computer interfaces is the interpretation of people’s behavior
In this paper we propose a way of using depth maps transformed into 3D point clouds to classify human activities
Complementing the mentioned applications related to hand gestures, these results can be seen as an argument for using the Viewpoint Feature Histogram descriptor (VFH) point cloud descriptors for people’s activity recognition
Summary
One of the most important tasks of human-computer interfaces is the interpretation of people’s behavior. One of the best known devices for acquiring depth maps is the Microsoft KinectTM sensor. The increase in the popularity of the KinectTM and ToF cameras has greatly contributed to an increased interest in using depth maps. This data can be considered as an aid, and as the main source of information. In this paper we propose a way of using depth maps transformed into 3D point clouds to classify human activities. We describe the activities as time sequences of feature vectors based on the Viewpoint. Feature Histogram descriptor (VFH) computed using the point cloud library (PCL) [7].
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