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Pedestrian Flow Tracking and Statistics of Monocular Camera Based on Convolutional Neural Network and Kalman Filter
He M(何淼)1,2,3,4,5; Luo HB(罗海波)1,2,4,5; Hui B(惠斌)1,2,4,5; Chang Z(常铮)1,2,4,5
Department光电信息技术研究室
Source PublicationAPPLIED SCIENCES-BASEL
ISSN2076-3417
2019
Volume9Issue:8Pages:1-13
Indexed BySCI
WOS IDWOS:000467316400109
Contribution Rank1
Funding OrganizationHainan Heaven Reward Security Technology Co. Ltd.
Keywordpedestrian flow statistics neural network Kalman filter multi-object tracking data association
AbstractPedestrian flow statistics and analysis in public places is an important means to ensure urban safety. However, in recent years, a video-based pedestrian flow statistics algorithm mainly relies on binocular vision or a vertical downward camera, which has serious limitations on the application scene and counting area, and cannot make use of the large number of monocular cameras in the city. To solve this problem, we propose a pedestrian flow statistics algorithm based on monocular camera. Firstly, a convolution neural network is used to detect the pedestrian targets. Then, with a Kalman filter, the motion models for the targets are established. Based on these motion models, data association algorithm completes target tracking. Finally, the pedestrian flow is counted by the pedestrian counting method based on virtual blocks. The algorithm is tested on real scenes and public data sets. The experimental results show that the algorithm has high accuracy and strong real-time performance, which verifies the reliability of the algorithm.
Language英语
WOS SubjectChemistry, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS Research AreaChemistry ; Materials Science ; Physics
Funding ProjectHainan Heaven Reward Security Technology Co. Ltd.
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/24944
Collection光电信息技术研究室
Corresponding AuthorHe M(何淼)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences,Shenyang 110016, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
4.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Science, Shenyang 110016, China
5.The Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China
Recommended Citation
GB/T 7714
He M,Luo HB,Hui B,et al. Pedestrian Flow Tracking and Statistics of Monocular Camera Based on Convolutional Neural Network and Kalman Filter[J]. APPLIED SCIENCES-BASEL,2019,9(8):1-13.
APA He M,Luo HB,Hui B,&Chang Z.(2019).Pedestrian Flow Tracking and Statistics of Monocular Camera Based on Convolutional Neural Network and Kalman Filter.APPLIED SCIENCES-BASEL,9(8),1-13.
MLA He M,et al."Pedestrian Flow Tracking and Statistics of Monocular Camera Based on Convolutional Neural Network and Kalman Filter".APPLIED SCIENCES-BASEL 9.8(2019):1-13.
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