Multi-View Indoor Human Detection Neural Network Based on Joint Learning

被引：0
作者：
Wang X. [1 ,2 ]
Zhang W. [1 ,2 ]
机构：
[1] School of Electrical Automation and Information Engineering, Tianjin University, Tianjin
[2] School of Microelectronics, Tianjin University, Tianjin
来源：
Guangxue Xuebao/Acta Optica Sinica | 2019年 / 39卷 / 02期
关键词：
Convolutional neural network; Image processing; Indoor human detection; Joint learning; Multi-view; Video surveillance;
D O I：
10.3788/AOS201939.0210002
中图分类号：
学科分类号：
摘要：
An indoor human detection dataset (IHDD) is established, and a novel multi-view indoor human detection neural network (MVNN) based on joint learning is proposed. The model consists of input data layer, feature extraction layer, deformation layer, visibility reasoning layer and classification layer, and the proposed MVNN algorithm can improve the detection performance when combined with the region proposal model and the multi-view model. Experimental results on the self-built IHDD show that compared with other existing detection algorithms, the proposed MVNN algorithm has a higher detection rate. It can still obtain good detection results even in the case of difficult situations such as various views, changing poses and occlusion for human targets, which indicates certain theoretical research value and practical value. © 2019, Chinese Lasers Press. All right reserved.
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