SCSS: An Intelligent Security System to Guard City Public Safe
Traditional security surveillance detection relies on post-event forensics or is hosted on a backend server, making it impossible to identify behaviors filmed in the field online. This paper proposes the Smart City Security System (SCSS) for detecting anomalous activity in public locations online. S...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10189831/ |
| _version_ | 1797768389583699968 |
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| author | Kun Xia Lingxiang Zhang Shuai Yuan Yang Lou |
| author_facet | Kun Xia Lingxiang Zhang Shuai Yuan Yang Lou |
| author_sort | Kun Xia |
| collection | DOAJ |
| description | Traditional security surveillance detection relies on post-event forensics or is hosted on a backend server, making it impossible to identify behaviors filmed in the field online. This paper proposes the Smart City Security System (SCSS) for detecting anomalous activity in public locations online. SCSS combines the DeepSORT and YOLOv4 algorithms to generate the DS-YOLO aberrant behavior detection algorithm, which compares and matches the target detected in the previous picture frame with the target detected in the following frame to achieve detection and tracking. SCSS is equipped with GPS, WIFI, and Uninterruptible Power Supply (UPS). When a risky behavior is detected, the system will upload the abnormal event as well as the latitude and longitude that occurred to the cloud via the WIFI and notify the user. The recognition accuracy of three deviant behaviors, including Fight, Car Accident, and Fall, was examined using diverse situations, and the results were 89%, 90%, and 90.33% respectively. The findings demonstrate that SCSS has successfully made the transition from passive monitoring to active identification, offsetting the flaws of conventional security systems that can only post-mordem forensics, and bridging the gap of the construction of national smart cities. |
| first_indexed | 2024-03-12T20:53:46Z |
| format | Article |
| id | doaj.art-253245bb4c874482a84023cabfd6a1eb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T20:53:46Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-253245bb4c874482a84023cabfd6a1eb2023-07-31T23:00:15ZengIEEEIEEE Access2169-35362023-01-0111764157642610.1109/ACCESS.2023.329764310189831SCSS: An Intelligent Security System to Guard City Public SafeKun Xia0Lingxiang Zhang1https://orcid.org/0009-0008-5647-8472Shuai Yuan2Yang Lou3Department of Electrical Engineering, University of Shanghai for Science and Technology, Yangpu, ChinaDepartment of Electrical Engineering, University of Shanghai for Science and Technology, Yangpu, ChinaDepartment of Electrical Engineering, University of Shanghai for Science and Technology, Yangpu, ChinaDepartment of Electrical Engineering, University of Shanghai for Science and Technology, Yangpu, ChinaTraditional security surveillance detection relies on post-event forensics or is hosted on a backend server, making it impossible to identify behaviors filmed in the field online. This paper proposes the Smart City Security System (SCSS) for detecting anomalous activity in public locations online. SCSS combines the DeepSORT and YOLOv4 algorithms to generate the DS-YOLO aberrant behavior detection algorithm, which compares and matches the target detected in the previous picture frame with the target detected in the following frame to achieve detection and tracking. SCSS is equipped with GPS, WIFI, and Uninterruptible Power Supply (UPS). When a risky behavior is detected, the system will upload the abnormal event as well as the latitude and longitude that occurred to the cloud via the WIFI and notify the user. The recognition accuracy of three deviant behaviors, including Fight, Car Accident, and Fall, was examined using diverse situations, and the results were 89%, 90%, and 90.33% respectively. The findings demonstrate that SCSS has successfully made the transition from passive monitoring to active identification, offsetting the flaws of conventional security systems that can only post-mordem forensics, and bridging the gap of the construction of national smart cities.https://ieeexplore.ieee.org/document/10189831/Intelligent security systemsimage recognitiondeep learningDS-YOLO |
| spellingShingle | Kun Xia Lingxiang Zhang Shuai Yuan Yang Lou SCSS: An Intelligent Security System to Guard City Public Safe IEEE Access Intelligent security systems image recognition deep learning DS-YOLO |
| title | SCSS: An Intelligent Security System to Guard City Public Safe |
| title_full | SCSS: An Intelligent Security System to Guard City Public Safe |
| title_fullStr | SCSS: An Intelligent Security System to Guard City Public Safe |
| title_full_unstemmed | SCSS: An Intelligent Security System to Guard City Public Safe |
| title_short | SCSS: An Intelligent Security System to Guard City Public Safe |
| title_sort | scss an intelligent security system to guard city public safe |
| topic | Intelligent security systems image recognition deep learning DS-YOLO |
| url | https://ieeexplore.ieee.org/document/10189831/ |
| work_keys_str_mv | AT kunxia scssanintelligentsecuritysystemtoguardcitypublicsafe AT lingxiangzhang scssanintelligentsecuritysystemtoguardcitypublicsafe AT shuaiyuan scssanintelligentsecuritysystemtoguardcitypublicsafe AT yanglou scssanintelligentsecuritysystemtoguardcitypublicsafe |