SMART SECURITY SURVEILLANCE SYSTEM USING IoT FOR CRIME DETECTION

Authors

  • Bhawna Computer Science and Engineering, Echelon Institute of Technology, Faridabad
  • Anupama Computer Science and Engineering, Echelon Institute of Technology, Faridabad
  • Gaurav Computer Science and Engineering, Echelon Institute of Technology, Faridabad
  • Yash Kumar Computer Science and Engineering, Echelon Institute of Technology, Faridabad
  • Dr. Monica Sankat Computer Science and Engineering, Echelon Institute of Technology, Faridabad

DOI:

https://doi.org/10.29121/ijetmr.v10.i5.2023.1603

Keywords:

Surveillance, System, Crime Detection, Iot, Smart Security

Abstract

With the growing need for enhanced security, traditional surveillance systems that rely on manual monitoring are no longer sufficient. Real-time, automated surveillance has become a critical area of computer vision, playing an increasingly vital role in public safety. Surveillance cameras, when paired with visible warning signs, can deter criminal activity by recording footage that assists in identifying and tracking individuals. Building on this foundation, integrating Wi-Fi-enabled IoT devices can significantly enhance system capabilities.
This project proposes a smart security surveillance system that leverages Convolutional Neural Networks (CNN) for crime detection and recognition tasks. Utilizing the ESP32-CAM module — an efficient IoT solution offering Wi-Fi connectivity and low-power communication — the system enables real-time analysis of surveillance footage. When a known individual is detected within the monitored area, the system promptly sends a notification. If an unfamiliar person or suspicious activity is detected through motion sensors and CNN-based analysis, the system issues an alert along with a captured video clip.

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References

Banerjee, T., et al. (2021). Ai-Based Video Surveillance: A Comprehensive Review. ACM Computing Surveys.

Choudhury, T., Consolvo, S. (2018). Real-Time Activity Detection With Wearable Sensors. Springer.

Das, R., et al. (2023). Smart Surveillance in Rural Areas: Challenges and Opportunities. Journal of ICT for Development.

Doyle, M. (2022). Developing Ethical AI Surveillance Frameworks. Surveillance & Society.

Expressif Systems. (2020). ESP32 Technical Reference Manual.

Gupta, S., et al. (2018). Integration of Ai in Digital Video Surveillance. Journal of Computer Vision.

Harari, E. (2016). Surveillance Ethics in the Digital Age. Ethics and Information Technology.

Jain, A., et al. (2017). Surveillance Technologies: A Historical Review. International Journal of Security Studies.

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet Classification With Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems (NIPS).

Krutz, R. L. (2005). Securing Scada Systems. Wiley Publishing.

Kumar, A., et al. (2021). Bias in AI Surveillance: Detection and Mitigation. AI & Society.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521, 436-444. https://doi.org/10.1038/nature14539

Lee, H., et al. (2019). Pedestrian Detection with CNNs in Smart Surveillance. IEEE Transactions on Image Processing.

Ministry of Human Resource Development, Government of India. (2018). Guidelines for School Safety.

National Crime Records Bureau (NCRB). (2020). Crime in India 2019. Ministry of Home Affairs, India.

Oyediran, O., et al. (2019). Review of Surveillance Systems. International Journal of Computer Applications.

Patel, N., et al. (2020). Real-Time AI Processing in Low-Power Environments. Embedded Systems Review.

Prasad, V., et al. (2020). Urban Crime Prevention Using Ai Surveillance. Journal of Urban Technology.

Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-Cnn: Towards Real-Time Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.2016.2577031

Roy, K., et al. (2020). Implementation of Iot Cameras for Low-Cost Surveillance. International Conference on IoT Applications.

Singh, P., et al. (2021). Face and Behavior Recognition In Crowds. Security and Privacy Journal.

Sundmaeker, H., Guillemin, P., Friess, P., & Woelfflé, S. (2010). Vision and Challenges for Realising the Internet of Things. CERP-IoT.

The Economic Times. (2020). Urban Crime Rates Rising Rapidly.

Thomas, B., et al. (2020). Data Security in Iot Surveillance Networks. Journal of Cybersecurity Research.

Viola, P., Jones, M. (2001). Rapid Object Detection Using A Boosted Cascade of Simple Features. Proceedings of CVPR.

Wang, L., et al. (2022). Threat Detection in Critical Infrastructure Using Smart Surveillance. International Journal of Secure Computing.

Welsh, B. C., Farrington, D. P. (2008). Effects of Closed-Circuit Television Surveillance on Crime. Campbell Systematic Reviews. https://doi.org/10.4073/csr.2008.17

Zhang, M., et al. (2019). The Role of IoT in Smart City Security. Sensors and Systems.

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Published

2023-05-30

How to Cite

Bhawna, Anupama, Gaurav, Kumar, Y., & Sankat, M. (2023). SMART SECURITY SURVEILLANCE SYSTEM USING IoT FOR CRIME DETECTION. International Journal of Engineering Technologies and Management Research, 10(5), 78–88. https://doi.org/10.29121/ijetmr.v10.i5.2023.1603