Abstract
Recently, the rapid development of inexpensive RGB-D sensor, like Microsoft Kinect, provides adequate information for human action recognition. In this paper, a recognition algorithm is presented in which feature representation is generated by concatenating spatial features from human contour of key frames and temporal features from time difference information of a sequence. Then, an improved multi-hidden layers extreme learning machine is introduced as classifier. At last, we test our scheme on the public UTD-MHAD dataset from recognition accuracy and time consumption.
Highlights
Action recognition has been a hot research topic due to its wild range of applications in many areas, such as intelligent video surveillance, smart living and human-computer interaction [1]-[3]
Hsu et al [16] introduced a new scheme by producing SpatioTemporal Matrix Intensity (STMI) from raw RGB and SpatioTemporal Matrix Depth (STMD) images from depth images respectively
HoG and HoF features were generated by constructing BoW-Pyramids, which made the classification of reversed actions become possible, such as from sit to stand and from stand to sit
Summary
Action recognition has been a hot research topic due to its wild range of applications in many areas, such as intelligent video surveillance, smart living and human-computer interaction [1]-[3]. Compared with traditional color sequence, depth sequence is invariant and stable to the illumination and body appearance It provides body structure and shape information for action classification. Unlike using information only from depth sequences, there are some methods combing multiple information to do action recognition, such as color data, skeleton data and depth maps. Two feature representation methods are introduced for action classification. Hsu et al [16] introduced a new scheme by producing SpatioTemporal Matrix Intensity (STMI) from raw RGB and SpatioTemporal Matrix Depth (STMD) images from depth images respectively This method was demonstrated to be viewinvariant.
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