Pedestrian Flow Tracking and Statistics of Monocular Camera Based on Convolutional Neural Network and Kalman Filter

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 光电信息技术研究室

	Pedestrian Flow Tracking and Statistics of Monocular Camera Based on Convolutional Neural Network and Kalman Filter
	Chang Z(常铮)1,2,4,5 ; 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
刊名	APPLIED SCIENCES-BASEL
	2019
卷号	9 期号:8 页码:1-13
关键词	pedestrian flow statistics neural network Kalman filter multi-object tracking data association
ISSN号	2076-3417
产权排序	1
英文摘要	Pedestrian 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.
资助项目	Hainan Heaven Reward Security Technology Co. Ltd.
WOS研究方向	Chemistry ; Materials Science ; Physics
语种	英语
WOS记录号	WOS:000467316400109
资助机构	Hainan Heaven Reward Security Technology Co. Ltd.
内容类型	期刊论文
源URL	[http://ir.sia.cn/handle/173321/24944]
专题	沈阳自动化研究所_光电信息技术研究室
通讯作者	He M(何淼)
作者单位	1.The Key Lab of Image Understanding and Computer Vision, Shenyang 110016, China 2.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Science, Shenyang 110016, China 3.University of Chinese Academy of Sciences, Beijing 100049, China 4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences,Shenyang 110016, China 5.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
推荐引用方式 GB/T 7714	Chang Z,He M,Luo HB,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	Chang Z,He M,Luo HB,&Hui B.(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	Chang Z,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.