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CCTV- Based Animal and Weapon Detection Alarm System

Ancy Sabu, Adithya C S, Ashly Mathew, Bipin Krishna D, Ansia S

Abstract


The current state of security and surveillance systems often lacks the capability of efficiently detecting and responding to potential threats such as intruding animals or individuals carrying weapons. This study utilizes closed-circuit television (CCTV) cameras to identify and respond to the presence of both animals and weapons in a monitored area. When an animal or weapon is identified, the system triggers an alarm, sending alerts to security personnel or property owners. The system aims to enhance security surveillance systems by integrating object detection models such as YOLOv3 (You Only Look Once) and Inceptionv3. The YOLOv3 model is employed for its efficiency in object detection, allowing fast and accurate identification of weapons within the camera's field of view. Meanwhile, the Inceptionv3 model is utilized for detecting animals in CCTV footage. Overall, this research presents a practical solution for improving security surveillance systems, offering advanced capabilities for detecting potential threats and mitigating security risks in diverse environments.


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References


. M. Ibraheam, K. F. Li and F. Gebali, "An Accurate and Fast Animal Species Detection System for Embedded Devices," in IEEE Access, 2023.

. Mr. R. Kumaran, S. Nivetha, C. Priyaand, P. Sangavi, "Animal Detection in Agri Land, " in IJCRT.ORG, 2023.

. H. Nguyen et al., "Animal Recognition and Identification with Deep Convolutional Neural Networks for Automated Wildlife Monitoring," 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Tokyo, Japan, 2017.

. A. Singh, et al., "Animal Detection in Man-made Environments," in 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass Village, CO, USA, 2020.

. Bhavana. C, Yashaswini.M, M. Tech Scholar, Assistant Professor, "Artificial Intelligence Based Birds and Animal Detection and Alert System" in IJCRT. ORG, 2022.

. M. T. Bhatti, M. G. Khan, M. Aslam and M. J. Fiaz, "Weapon Detection in Real Time CCTV Videos Using Deep Learning," in IEEE Access, 2021.

. K. He, X. Zhang, S. Ren, and J. Sun, ‘‘Deep residual learning for image recognition,” in Proc. IEEE Conf. Computer Vision Pattern Recognition. (CVPR), 2016.

. R. Girshick, J. Donahue, T. Darrell, and J. Malik, ‘‘Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proc. IEEE Conf. Computer Vision Pattern Recognition, 2014.

. S. B. Kotsiantis, D. Kanellopoulos, and P. E. Pintelas, “Data preprocessing for supervised learning,” 2014.

. T. Darker, A. G. Gale, and A. Blechko, “CCTV as an automated sensor for firearms detection: Human-derived performance as a precursor to automatic recognition,’’ Proc. SPIE, 2008.


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