Suspicious activity trigger system using YOLOv6 convolutional neural network
Property theft is one of the crimes that increases in which leads to a major concern in Malaysia. Despite of having surveillance cameras (CCTV) everywhere, the crimes keep occur due to the lack of security system. The security system can be developed by utilizing the existence of CCTVs specifically...
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| Format: | Conference or Workshop Item |
| Language: | English English |
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2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/38780/ http://umpir.ump.edu.my/id/eprint/38780/1/Suspicious%20Activity%20Trigger%20System%20using%20YOLOv6%20Convolutional%20Neural%20Network.pdf http://umpir.ump.edu.my/id/eprint/38780/2/Suspicious%20activity%20trigger%20system%20using%20YOLOv6%20convolutional%20neural%20network_ABS.pdf |
| _version_ | 1848825596277161984 |
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| author | Suryanti, Awang Mohd Qhairel, Rafiqi Rokei Sulaiman, Junaida |
| author_facet | Suryanti, Awang Mohd Qhairel, Rafiqi Rokei Sulaiman, Junaida |
| author_sort | Suryanti, Awang |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Property theft is one of the crimes that increases in which leads to a major concern in Malaysia. Despite of having surveillance cameras (CCTV) everywhere, the crimes keep occur due to the lack of security system. The security system can be developed by utilizing the existence of CCTVs specifically home surveillance CCTV. Therefore, this paper introduces a security system known as Suspicious Activity Trigger System (SATS) that able to automatically trigger an alarm or an alert message whenever suspicious activity is detected from the CCTV video image. The activity will be detected in a video image using Deep Learning technique which is YOLOv6 Convolutional Neural Network (CNN) algorithm. The algorithm will detect an object which is a person in the video and classify it as a suspicious activity or not. If the activity is classified as the suspicious activity, the system will automatically display a trigger message to alert SATS user. The user can therefore take whatever appropriate measure to prevent being a victim. Experiments have been conducted using a dataset taken from Google Open Image. We also implemented the experiments on the self-obtained dataset. Based on the experiment, 92.53% for precision and 96.6% of the accuracy is obtained using this algorithm. Therefore, YOLOv6 can be implemented in the security system to prevent crimes in residency areas. |
| first_indexed | 2025-11-15T03:31:26Z |
| format | Conference or Workshop Item |
| id | ump-38780 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:31:26Z |
| publishDate | 2023 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-387802023-11-06T06:59:37Z http://umpir.ump.edu.my/id/eprint/38780/ Suspicious activity trigger system using YOLOv6 convolutional neural network Suryanti, Awang Mohd Qhairel, Rafiqi Rokei Sulaiman, Junaida QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Property theft is one of the crimes that increases in which leads to a major concern in Malaysia. Despite of having surveillance cameras (CCTV) everywhere, the crimes keep occur due to the lack of security system. The security system can be developed by utilizing the existence of CCTVs specifically home surveillance CCTV. Therefore, this paper introduces a security system known as Suspicious Activity Trigger System (SATS) that able to automatically trigger an alarm or an alert message whenever suspicious activity is detected from the CCTV video image. The activity will be detected in a video image using Deep Learning technique which is YOLOv6 Convolutional Neural Network (CNN) algorithm. The algorithm will detect an object which is a person in the video and classify it as a suspicious activity or not. If the activity is classified as the suspicious activity, the system will automatically display a trigger message to alert SATS user. The user can therefore take whatever appropriate measure to prevent being a victim. Experiments have been conducted using a dataset taken from Google Open Image. We also implemented the experiments on the self-obtained dataset. Based on the experiment, 92.53% for precision and 96.6% of the accuracy is obtained using this algorithm. Therefore, YOLOv6 can be implemented in the security system to prevent crimes in residency areas. 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/38780/1/Suspicious%20Activity%20Trigger%20System%20using%20YOLOv6%20Convolutional%20Neural%20Network.pdf pdf en http://umpir.ump.edu.my/id/eprint/38780/2/Suspicious%20activity%20trigger%20system%20using%20YOLOv6%20convolutional%20neural%20network_ABS.pdf Suryanti, Awang and Mohd Qhairel, Rafiqi Rokei and Sulaiman, Junaida (2023) Suspicious activity trigger system using YOLOv6 convolutional neural network. In: 5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023 , 20-23 February 2023 , Virtual, Online. pp. 527-532.. ISBN 978-166545645-6 (Published) https://doi.org/10.1109/ICAIIC57133.2023.10066970 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Suryanti, Awang Mohd Qhairel, Rafiqi Rokei Sulaiman, Junaida Suspicious activity trigger system using YOLOv6 convolutional neural network |
| title | Suspicious activity trigger system using YOLOv6 convolutional neural network |
| title_full | Suspicious activity trigger system using YOLOv6 convolutional neural network |
| title_fullStr | Suspicious activity trigger system using YOLOv6 convolutional neural network |
| title_full_unstemmed | Suspicious activity trigger system using YOLOv6 convolutional neural network |
| title_short | Suspicious activity trigger system using YOLOv6 convolutional neural network |
| title_sort | suspicious activity trigger system using yolov6 convolutional neural network |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/38780/ http://umpir.ump.edu.my/id/eprint/38780/ http://umpir.ump.edu.my/id/eprint/38780/1/Suspicious%20Activity%20Trigger%20System%20using%20YOLOv6%20Convolutional%20Neural%20Network.pdf http://umpir.ump.edu.my/id/eprint/38780/2/Suspicious%20activity%20trigger%20system%20using%20YOLOv6%20convolutional%20neural%20network_ABS.pdf |