Pedestrian Flow Tracking and Statistics of Monocular Camera Based on Convolutional Neural Network and Kalman Filter
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/8/1624 |
| _version_ | 1818515113413443584 |
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| author | Miao He Haibo Luo Bin Hui Zheng Chang |
| author_facet | Miao He Haibo Luo Bin Hui Zheng Chang |
| author_sort | Miao He |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T00:24:44Z |
| format | Article |
| id | doaj.art-06f44cae08d24f8dbdd183cf2354c7d1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-11T00:24:44Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-06f44cae08d24f8dbdd183cf2354c7d12022-12-22T01:27:35ZengMDPI AGApplied Sciences2076-34172019-04-0198162410.3390/app9081624app9081624Pedestrian Flow Tracking and Statistics of Monocular Camera Based on Convolutional Neural Network and Kalman FilterMiao He0Haibo Luo1Bin Hui2Zheng Chang3Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaShenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaShenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaShenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaPedestrian 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.https://www.mdpi.com/2076-3417/9/8/1624pedestrian flow statisticsneural networkKalman filtermulti-object trackingdata association |
| spellingShingle | Miao He Haibo Luo Bin Hui Zheng Chang Pedestrian Flow Tracking and Statistics of Monocular Camera Based on Convolutional Neural Network and Kalman Filter Applied Sciences pedestrian flow statistics neural network Kalman filter multi-object tracking data association |
| title | Pedestrian Flow Tracking and Statistics of Monocular Camera Based on Convolutional Neural Network and Kalman Filter |
| title_full | Pedestrian Flow Tracking and Statistics of Monocular Camera Based on Convolutional Neural Network and Kalman Filter |
| title_fullStr | Pedestrian Flow Tracking and Statistics of Monocular Camera Based on Convolutional Neural Network and Kalman Filter |
| title_full_unstemmed | Pedestrian Flow Tracking and Statistics of Monocular Camera Based on Convolutional Neural Network and Kalman Filter |
| title_short | Pedestrian Flow Tracking and Statistics of Monocular Camera Based on Convolutional Neural Network and Kalman Filter |
| title_sort | pedestrian flow tracking and statistics of monocular camera based on convolutional neural network and kalman filter |
| topic | pedestrian flow statistics neural network Kalman filter multi-object tracking data association |
| url | https://www.mdpi.com/2076-3417/9/8/1624 |
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