[1]MO Hongwei,WANG Haibo.Research on human behavior detection based on Faster R-CNN[J].CAAI Transactions on Intelligent Systems,2018,13(6):967-973.[doi:10.11992/tis.201801025]
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Research on human behavior detection based on Faster R-CNN

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 6 Page number: 967-973 Column: 学术论文—机器学习 Public date: 2018-10-25
Title:
Research on human behavior detection based on Faster R-CNN
Author(s):
MO Hongwei;  WANG Haibo
College of Automation, Harbin Engineering University, Harbin 150001, China
Keywords:
human behavior detection; faster R-CNN; OHEM; deep learning; object detection; convolutional neural network; batch normalization; transfer learning
CLC:
TP181
DOI:
10.11992/tis.201801025
Abstract:
Because of large intra-class difference and large inter-class similarity of human behaviors, as well as problems such as visual angle and occlusion, it is difficult to extract features, especially effective features, using the manual feature extraction method. This results in low accuracy of human behavior detection. To solve this problem, this paper applies a faster region-based convolutional neural network (Faster R-CNN) algorithm, which has a better detection effect, to detect human behavior on the basis of object detection. By combining the Faster-RCNN algorithm with batch normalization algorithm and an online hard example mining algorithm, the deep learning algorithm is effectively utilized to detect human behavior. Experimental results show that the accuracy of classification and position of the improved algorithm exceeds 80%, thereby verifying its high recognition accuracy.
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