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A novel framework for automated monitoring and analysis of high density pedestrian flow
| 题名： | A novel framework for automated monitoring and analysis of high density pedestrian flow |
| 正文语种： | 英文 |
| 作者： | Muhammad Baqui;Manar D. Samad;Rainaid Lohner |
| 作者单位： | George Mason University;Tennessee State University |
| 关键词： | Crowd density; high density pedestrian flow; particle image velocimetry; pedestrian traffic;video tracking |
| 摘要： | Pedestrian traffic is an important subject of surveillanee to ensure public safety and traffic management, which may benefit from intellige nt and continuous analysis of pedestrian videos. State-of-the-art methods for intelligent pedestrian surveillance have a number of limitations in automati ng and derivi ng useful in formation of high-density pedestrian traffic (HDPT) using closed circuit television (CCTV) images. This work introduces an automatic and improved HDPT surveillance system by integrating and optimizing multiple computati onal steps to predict pedestrian distribution from in put video frames. A fast and efficie nt particle image velocimetry (PIV) technique is proposed to yield pedestrian velocities. A machine learning regressor model, boosted Ferns, is used to improve pedestrian count and density estimation: an essential metric for HDPT analysis. A camera perspective model is proposed to improve the speed and position estimates of HDPT by projecting 2D image pixels to 3D world-coordinate dat. All these functional improvements in HDPT velocity and displacement estimations are used as inputs to a sophisticated pedestria n flow evolution model, PEDFLOW to predict HDPT distribution at a future time point, which is a crucial information for pedestrian traffic management. The predicted and simulated HDPT properties (density, velocity) obtained using the proposed framework show low errors when compared to the ground truth data. The proposed framework is computationally efficient, suitable for multiple camera feeds with HDPT videos, and capable of rapidly analyzing and predicting flows of thousands of pedestrians. The paper shows one of the first steps towards fully integrated CCTV-based automated HDPT management system. |
| 出版日期： | 2020 |
| 出版年： | 2020 |
| 期刊名称： | Journal of Intelligent Transportation Systems Technology Planning and Operations |
| 卷： | Vol24 |
| 期： | No01-06 |
| 页码： | 585-597 |
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