Action Recognition using Key-Frame Features of Depth Sequence and ELM

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.