Abstract
In order to efficiently extract and encode 3D information of human action from depth images, we present a feature extraction and recognition method based on depth video sequences. First, depth images are projected continuously onto three planes of Cartesian coordinate system, and differential images of the respective projection surfaces are accumulated to obtain the complete 3D information of the depth motion maps (DMMs). Then, discriminative completed LBP (disCLBP) encodes depth motion maps to extract effective human action information. A hybrid classifier combined with Extreme Learning Machine (ELM) and collaborative representation classification (CRC) is employed to reduce the computational complexity while reducing the impact of noise. The proposed method is tested on the MSR-Action3D database; the experimental results show that it achieves 96.0% accuracy and well performs better robustness comparing to other popular approaches.
Highlights
Human action recognition is an important and challenging topic in the field of computer vision
In order to obtain the trade-off between recognition accuracy and computational complexity, in this paper, we propose a framework for human action recognition
Our method is using discriminative completed local binary pattern based on depth motion maps as feature and is using a hybrid classifier combined with Extreme Learning Machine (ELM) and collaborative representation classification (CRC) to finish the classification
Summary
Human action recognition is an important and challenging topic in the field of computer vision. In [5], a hybrid hidden Markov model is utilized to solve multiview problems, and it combines shape and motion optical flow to classify motions This method has the advantages of simple calculation and getting parameters such as human position and size of human appearance . Fitzgibbon [6] uses the Microsoft somatosensory device Kinect to propose a method for estimating the position of skeletal joints by extracting the shape information of human motion. In [10], authors use Local Binary Pattern and Extreme Learning Machine to identify human action This approach achieves well performance on the testing data set. Our method is using discriminative completed local binary pattern based on depth motion maps as feature and is using a hybrid classifier combined with Extreme Learning Machine (ELM) and collaborative representation classification (CRC) to finish the classification.
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