Research Article
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Preventing Crime and Terrorist Activities with a New Anomaly Detection Approach Based on Outfit

Year 2023, Volume: 7 Issue: 2, 167 - 182, 20.12.2023

Gizem Ortaç Koşun Seçkin Yılmaz Yusuf Kayıpmaz Rüya Şamlı

https://doi.org/10.38088/jise.1348213

Abstract

Video surveillance systems play an important role in ensuring security indoors and outdoors and detecting suspicious persons due to the increasing violence and terrorist acts every year. In the proposed study, an artificial intelligence-based warning system has been developed, which enables the detection of potential suspects who may carry out criminal or terrorist activities by detecting anomalies in surveillance videos. In this developed system, an abnormality is detected by using the outfits of the people. The YOLOv7 object detection model is trained on our customized data sets, and suspicious person detection is made through outfit information. Especially in cases where biometric data is hidden, dress information makes it easier to obtain information about people. For this reason, the knowledge of outfits is the main point of this study in the detection of suspicious persons. Thanks to this study, security guards will be able to focus on this suspicious person before they pre-empt any crime or terrorist activity. If there are other data confirming the suspicious situation as a result of this follow-up; security personnel will have time to eliminate the crime or attack. The experimental results obtained have been promising in terms of the usability of a person's outfit anomalies to ensure public confidence or avoid risk to human life. Although there are various studies in the literature for the prevention of terrorist or criminal activities; there is no study in which people's outfit is used to identify suspects.

Keywords

Forensic science Anomaly detection Soft biometrics Surveillance video

References

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  • [2] Kamthe, U. M., & Patil, C. G. (2018, August). Suspicious activity recognition in video surveillance system. In 2018 Fourth international conference on computing communication control and automation (ICCUBEA) (pp. 1-6). IEEE.
  • [3] Lavee, G., Khan, L., & Thuraisingham, B. (2005, August). A framework for a video analysis tool for suspicious event detection. In Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data (pp. 79-84).
  • [4] Bedeli, M., Geradts, Z., & van Eijk, E. (2018). Clothing identification via deep learning: forensic applications. Forensic sciences research, 3(3), 219-229.
  • [5] Chen, Q., Huang, J., Feris, R., Brown, L. M., Dong, J., & Yan, S. (2015). Deep domain adaptation for describing people based on fine-grained clothing attributes. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5315-5324).
  • [6] Zapotosky, M. (2021). FBI releases new footage of possible RNC, DNC pipe-bomb suspect, says person probably'not from the area'. The Washington Post, NA-NA.
  • [7] Narejo, S., Pandey, B., Esenarro Vargas, D., Rodriguez, C., & Anjum, M. R. (2021). Weapon detection using YOLO V3 for smart surveillance system. Mathematical Problems in Engineering, 2021, 1-9.
  • [8] Grega, M., Matiolański, A., Guzik, P., & Leszczuk, M. (2016). Automated detection of firearms and knives in a CCTV image. Sensors, 16(1), 47.
  • [9] Mehta, P., Kumar, A., & Bhattacharjee, S. (2020, July). Fire and gun violence based anomaly detection system using deep neural networks. In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 199-204). IEEE.
  • [10] Marbach, G., Loepfe, M., & Brupbacher, T. (2006). An image processing technique for fire detection in video images. Fire safety journal, 41(4), 285-289.
  • [11] Dever, J., da Vitoria Lobo, N., & Shah, M. (2002, August). Automatic visual recognition of armed robbery. In 2002 International Conference on Pattern Recognition (Vol. 1, pp. 451-455). IEEE.
  • [12] Yin, J. H., Velastin, S. A., & Davies, A. C. (1996). Image processing techniques for crowd density estimation using a reference image. In Recent Developments in Computer Vision: Second Asian Conference on Computer Vision, ACCV'95 Singapore, December 5–8, 1995 Invited Session Papers 2 (pp. 489-498). Springer Berlin Heidelberg.
  • [13] Ravanbakhsh, M., Nabi, M., Sangineto, E., Marcenaro, L., Regazzoni, C., & Sebe, N. (2017, September). Abnormal event detection in videos using generative adversarial nets. In 2017 IEEE international conference on image processing (ICIP) (pp. 1577-1581). IEEE.
  • [14] Mehran, R., Oyama, A., & Shah, M. (2009, June). Abnormal crowd behavior detection using social force model. In 2009 IEEE conference on computer vision and pattern recognition (pp. 935-942). IEEE.
  • [15] Heartexlabs. Heartexlabs/labelimg: LabelImg is now part of the label Studio Community. the popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out label studio, the open-source data labeling tool for images, text, hypertext, audio, video and time-series data.
  • [16] Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
  • [17] Atik, M. E., Duran, Z., & ÖZGÜNLÜK, R. (2022). Comparison of YOLO versions for object detection from aerial images. International Journal of Environment and Geoinformatics, 9(2), 87-93.
  • [18] Yang, F., Zhang, X., & Liu, B. (2022). Video object tracking based on YOLOv7 and DeepSORT. arXiv preprint arXiv:2207.12202.
  • [19] Hussain, M., Al-Aqrabi, H., Munawar, M., Hill, R., & Alsboui, T. (2022). Domain feature mapping with YOLOv7 for automated edge-based pallet racking inspections. Sensors, 22(18), 6927.
  • [20] Wang, C. Y., Bochkovskiy, A., & Liao, H. Y. M. (2023). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7464-7475).
  • [21] Soudy, M., Afify, Y., & Badr, N. (2022). RepConv: A novel architecture for image scene classification on Intel scenes dataset. International Journal of Intelligent Computing and Information Sciences, 22(2), 63-73.
  • [23] Zheng, J., Wu, H., Zhang, H., Wang, Z., & Xu, W. (2022). Insulator-defect detection algorithm based on improved YOLOv7. Sensors, 22(22), 8801.
  • [24] Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning deep features for discriminative localization. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2921-2929).

Year 2023, Volume: 7 Issue: 2, 167 - 182, 20.12.2023

Gizem Ortaç Koşun Seçkin Yılmaz Yusuf Kayıpmaz Rüya Şamlı

https://doi.org/10.38088/jise.1348213

Abstract

References

  • [1] Xiao, J., Li, S., & Xu, Q. (2019). Video-based evidence analysis and extraction in digital forensic investigation. IEEE Access, 7, 55432-55442.
  • [2] Kamthe, U. M., & Patil, C. G. (2018, August). Suspicious activity recognition in video surveillance system. In 2018 Fourth international conference on computing communication control and automation (ICCUBEA) (pp. 1-6). IEEE.
  • [3] Lavee, G., Khan, L., & Thuraisingham, B. (2005, August). A framework for a video analysis tool for suspicious event detection. In Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data (pp. 79-84).
  • [4] Bedeli, M., Geradts, Z., & van Eijk, E. (2018). Clothing identification via deep learning: forensic applications. Forensic sciences research, 3(3), 219-229.
  • [5] Chen, Q., Huang, J., Feris, R., Brown, L. M., Dong, J., & Yan, S. (2015). Deep domain adaptation for describing people based on fine-grained clothing attributes. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5315-5324).
  • [6] Zapotosky, M. (2021). FBI releases new footage of possible RNC, DNC pipe-bomb suspect, says person probably'not from the area'. The Washington Post, NA-NA.
  • [7] Narejo, S., Pandey, B., Esenarro Vargas, D., Rodriguez, C., & Anjum, M. R. (2021). Weapon detection using YOLO V3 for smart surveillance system. Mathematical Problems in Engineering, 2021, 1-9.
  • [8] Grega, M., Matiolański, A., Guzik, P., & Leszczuk, M. (2016). Automated detection of firearms and knives in a CCTV image. Sensors, 16(1), 47.
  • [9] Mehta, P., Kumar, A., & Bhattacharjee, S. (2020, July). Fire and gun violence based anomaly detection system using deep neural networks. In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 199-204). IEEE.
  • [10] Marbach, G., Loepfe, M., & Brupbacher, T. (2006). An image processing technique for fire detection in video images. Fire safety journal, 41(4), 285-289.
  • [11] Dever, J., da Vitoria Lobo, N., & Shah, M. (2002, August). Automatic visual recognition of armed robbery. In 2002 International Conference on Pattern Recognition (Vol. 1, pp. 451-455). IEEE.
  • [12] Yin, J. H., Velastin, S. A., & Davies, A. C. (1996). Image processing techniques for crowd density estimation using a reference image. In Recent Developments in Computer Vision: Second Asian Conference on Computer Vision, ACCV'95 Singapore, December 5–8, 1995 Invited Session Papers 2 (pp. 489-498). Springer Berlin Heidelberg.
  • [13] Ravanbakhsh, M., Nabi, M., Sangineto, E., Marcenaro, L., Regazzoni, C., & Sebe, N. (2017, September). Abnormal event detection in videos using generative adversarial nets. In 2017 IEEE international conference on image processing (ICIP) (pp. 1577-1581). IEEE.
  • [14] Mehran, R., Oyama, A., & Shah, M. (2009, June). Abnormal crowd behavior detection using social force model. In 2009 IEEE conference on computer vision and pattern recognition (pp. 935-942). IEEE.
  • [15] Heartexlabs. Heartexlabs/labelimg: LabelImg is now part of the label Studio Community. the popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out label studio, the open-source data labeling tool for images, text, hypertext, audio, video and time-series data.
  • [16] Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
  • [17] Atik, M. E., Duran, Z., & ÖZGÜNLÜK, R. (2022). Comparison of YOLO versions for object detection from aerial images. International Journal of Environment and Geoinformatics, 9(2), 87-93.
  • [18] Yang, F., Zhang, X., & Liu, B. (2022). Video object tracking based on YOLOv7 and DeepSORT. arXiv preprint arXiv:2207.12202.
  • [19] Hussain, M., Al-Aqrabi, H., Munawar, M., Hill, R., & Alsboui, T. (2022). Domain feature mapping with YOLOv7 for automated edge-based pallet racking inspections. Sensors, 22(18), 6927.
  • [20] Wang, C. Y., Bochkovskiy, A., & Liao, H. Y. M. (2023). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7464-7475).
  • [21] Soudy, M., Afify, Y., & Badr, N. (2022). RepConv: A novel architecture for image scene classification on Intel scenes dataset. International Journal of Intelligent Computing and Information Sciences, 22(2), 63-73.
  • [23] Zheng, J., Wu, H., Zhang, H., Wang, Z., & Xu, W. (2022). Insulator-defect detection algorithm based on improved YOLOv7. Sensors, 22(22), 8801.
  • [24] Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning deep features for discriminative localization. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2921-2929).
There are 23 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence (Other)
Journal Section Research Articles
Authors

Gizem Ortaç Koşun 0000-0003-1228-9852

Seçkin Yılmaz 0000-0001-6791-1536

Yusuf Kayıpmaz 0000-0002-4588-8715

Rüya Şamlı 0000-0002-8723-1228

Early Pub Date December 6, 2023
Publication Date December 20, 2023
Published in Issue Year 2023Volume: 7 Issue: 2

Cite

APA Ortaç Koşun, G., Yılmaz, S., Kayıpmaz, Y., Şamlı, R. (2023). Preventing Crime and Terrorist Activities with a New Anomaly Detection Approach Based on Outfit. Journal of Innovative Science and Engineering, 7(2), 167-182. https://doi.org/10.38088/jise.1348213
AMA Ortaç Koşun G, Yılmaz S, Kayıpmaz Y, Şamlı R. Preventing Crime and Terrorist Activities with a New Anomaly Detection Approach Based on Outfit. JISE. December 2023;7(2):167-182. doi:10.38088/jise.1348213
Chicago Ortaç Koşun, Gizem, Seçkin Yılmaz, Yusuf Kayıpmaz, and Rüya Şamlı. “Preventing Crime and Terrorist Activities With a New Anomaly Detection Approach Based on Outfit”. Journal of Innovative Science and Engineering 7, no. 2 (December 2023): 167-82. https://doi.org/10.38088/jise.1348213.
EndNote Ortaç Koşun G, Yılmaz S, Kayıpmaz Y, Şamlı R (December 1, 2023) Preventing Crime and Terrorist Activities with a New Anomaly Detection Approach Based on Outfit. Journal of Innovative Science and Engineering 7 2 167–182.
IEEE G. Ortaç Koşun, S. Yılmaz, Y. Kayıpmaz, and R. Şamlı, “Preventing Crime and Terrorist Activities with a New Anomaly Detection Approach Based on Outfit”, JISE, vol. 7, no. 2, pp. 167–182, 2023, doi: 10.38088/jise.1348213.
ISNAD Ortaç Koşun, Gizem et al. “Preventing Crime and Terrorist Activities With a New Anomaly Detection Approach Based on Outfit”. Journal of Innovative Science and Engineering 7/2 (December 2023), 167-182. https://doi.org/10.38088/jise.1348213.
JAMA Ortaç Koşun G, Yılmaz S, Kayıpmaz Y, Şamlı R. Preventing Crime and Terrorist Activities with a New Anomaly Detection Approach Based on Outfit. JISE. 2023;7:167–182.
MLA Ortaç Koşun, Gizem et al. “Preventing Crime and Terrorist Activities With a New Anomaly Detection Approach Based on Outfit”. Journal of Innovative Science and Engineering, vol. 7, no. 2, 2023, pp. 167-82, doi:10.38088/jise.1348213.
Vancouver Ortaç Koşun G, Yılmaz S, Kayıpmaz Y, Şamlı R. Preventing Crime and Terrorist Activities with a New Anomaly Detection Approach Based on Outfit. JISE. 2023;7(2):167-82.
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