Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 10
3192-3195,3200

Behavior recognition algorithm based on DRN and Faster R-CNN fusion model

Yang Nan
Yang Shen
Du Neng
School of Information Science & Engineering, Wuhan University of Science & Technology, Wuhan 430081, China
DOI: 10.19734/j.issn.1001-3695.2018.05.0354

Abstract

Due to the traditional single person behavior recognition algorithm is easily affected by the diversity, background and illumination of pedestrians based on the accuracy of DRN in classification and detection network Faster R-CNN in target tracking, this paper proposed a fusion network model composed of DRN and Faster R-CNN. The model integrated with dilated convolution residual in Faster R-CNN to replace the original convolution layer. It also made two improvements to the fusion model. It added a batch normalization layer in front of each layer and used three levels of dilated convolution residual blocks to instead of partial two levels of residual blocks. The experimental results show that the three fusion network recognition algorithms proposed in this paper have achieved a higher mAP than other behavior recognition algorithms on the Olympic sports dataset. Among them, the fusion model with three layers of convolution residual blocks has the best recognition performance, and mAP achieves 78.9%.

Foundation Support

国家自然科学基金资助项目(61502358)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.05.0354
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 10
Section: Technology of Graphic & Image
Pages: 3192-3195,3200
Serial Number: 1001-3695(2019)10-070-3192-04

Publish History

[2019-10-05] Printed Article

Cite This Article

杨楠, 杨莘, 杜能. 基于DRN和Faster R-CNN融合模型的行为识别算法 [J]. 计算机应用研究, 2019, 36 (10): 3192-3195,3200. (Yang Nan, Yang Shen, Du Neng. Behavior recognition algorithm based on DRN and Faster R-CNN fusion model [J]. Application Research of Computers, 2019, 36 (10): 3192-3195,3200. )

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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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