Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera
For public safety and physical security, currently more than a billion closed-circuit television (CCTV) cameras are in use around the world. Proliferation of artificial intelligence (AI) and machine/deep learning (M/DL) technologies have gained significant applications including crowd surveillance....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/10/11/143 |
| _version_ | 1797510730115710976 |
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| author | Alem Fitwi Yu Chen Han Sun Robert Harrod |
| author_facet | Alem Fitwi Yu Chen Han Sun Robert Harrod |
| author_sort | Alem Fitwi |
| collection | DOAJ |
| description | For public safety and physical security, currently more than a billion closed-circuit television (CCTV) cameras are in use around the world. Proliferation of artificial intelligence (AI) and machine/deep learning (M/DL) technologies have gained significant applications including crowd surveillance. The state-of-the-art distance and area estimation algorithms either need multiple cameras or a reference object as a ground truth. It is an open question to obtain an estimation using a single camera without a scale reference. In this paper, we propose a novel solution called E-SEC, which estimates interpersonal distance between a pair of dynamic human objects, area occupied by a dynamic crowd, and density using a single edge camera. The E-SEC framework comprises edge CCTV cameras responsible for capturing a crowd on video frames leveraging a customized YOLOv3 model for human detection. E-SEC contributes an interpersonal distance estimation algorithm vital for monitoring the social distancing of a crowd, and an area estimation algorithm for dynamically determining an area occupied by a crowd with changing size and position. A unified output module generates the crowd size, interpersonal distances, social distancing violations, area, and density per every frame. Experimental results validate the accuracy and efficiency of E-SEC with a range of different video datasets. |
| first_indexed | 2024-03-10T05:35:28Z |
| format | Article |
| id | doaj.art-733671c87e3f4711ac28cf5ade1a5967 |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-10T05:35:28Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-733671c87e3f4711ac28cf5ade1a59672023-11-22T22:56:58ZengMDPI AGComputers2073-431X2021-11-01101114310.3390/computers10110143Estimating Interpersonal Distance and Crowd Density with a Single-Edge CameraAlem Fitwi0Yu Chen1Han Sun2Robert Harrod3Department of Electrical and Computer Engineering, Binghamton University, Binghamton, NY 13902, USADepartment of Electrical and Computer Engineering, Binghamton University, Binghamton, NY 13902, USADepartment of Electrical and Computer Engineering, Binghamton University, Binghamton, NY 13902, USATechnergetics, LLC, Utica, NY 13502, USAFor public safety and physical security, currently more than a billion closed-circuit television (CCTV) cameras are in use around the world. Proliferation of artificial intelligence (AI) and machine/deep learning (M/DL) technologies have gained significant applications including crowd surveillance. The state-of-the-art distance and area estimation algorithms either need multiple cameras or a reference object as a ground truth. It is an open question to obtain an estimation using a single camera without a scale reference. In this paper, we propose a novel solution called E-SEC, which estimates interpersonal distance between a pair of dynamic human objects, area occupied by a dynamic crowd, and density using a single edge camera. The E-SEC framework comprises edge CCTV cameras responsible for capturing a crowd on video frames leveraging a customized YOLOv3 model for human detection. E-SEC contributes an interpersonal distance estimation algorithm vital for monitoring the social distancing of a crowd, and an area estimation algorithm for dynamically determining an area occupied by a crowd with changing size and position. A unified output module generates the crowd size, interpersonal distances, social distancing violations, area, and density per every frame. Experimental results validate the accuracy and efficiency of E-SEC with a range of different video datasets.https://www.mdpi.com/2073-431X/10/11/143area estimationcrowd managementCOVID-19edge camerainterpersonal distancesocial distancing |
| spellingShingle | Alem Fitwi Yu Chen Han Sun Robert Harrod Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera Computers area estimation crowd management COVID-19 edge camera interpersonal distance social distancing |
| title | Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera |
| title_full | Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera |
| title_fullStr | Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera |
| title_full_unstemmed | Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera |
| title_short | Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera |
| title_sort | estimating interpersonal distance and crowd density with a single edge camera |
| topic | area estimation crowd management COVID-19 edge camera interpersonal distance social distancing |
| url | https://www.mdpi.com/2073-431X/10/11/143 |
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