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Stream/Area:
-
Subject:
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Paper ID:
IJSREM65795
Volume:
10 |
7 |
Jul 2026
Pages:
1-3
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Science and Engineering
Vidarbha Institute of Technology
Computer Science and Engineering
Vidarbha Institute of Technology
Computer Science and Engineering
Vidarbha Institute of Technology
Computer Science and Engineering
Vidarbha Institute of Technology
Computer Science and Engineering
Vidarbha Institute of Technology
Human-wildlife conflict has increased significantly in forest-border and rural regions. This research presents an AI-Based Wildlife Surveillance and Animal Threat Detection System using the YOLOv10 deep learning architecture for real-time detection of tiger, leopard, and lion. The system integrates object detection, object tracking, snapshot generation, SQLite database logging, timestamp recording, and automated email alerts into a unified surveillance platform. A custom dataset collected from Roboflow Universe consisting of approximately 2,000 tiger images, 700 lion images, and 500+ leopard images was used for model training. The trained YOLOv10s model demonstrated efficient real-time wildlife detection performance suitable for forest surveillance and remote monitoring applications
KEYWORDS
The proposed AI-Based Wildlife Surveillance and Animal Threat Detection System successfully integrates YOLOv10-based real-time animal detection with intelligent monitoring features. The system improves wildlife surveillance efficiency by providing automated alerts, database management, object tracking, and realtime monitoring. The developed solution can be deployed in forest-border regions, agricultural areas, and remote wildlife monitoring environments.
1. You Only Look Once: Unified, Real-Time Object Detection J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 779–788. 2. Simple Online and Realtime Tracking with a Deep Association Metric N. Wojke, A. Bewley, and D. Paulus, "Simple Online and Realtime Tracking with a Deep Association Metric," in 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China, 2017, pp. 3645–3649. 3. ByteTrack: Multi-Object Tracking by Associating Every Detection Box Y. Zhang, P. Sun, Y. Jiang, D. Yu, F. Weng, Z. Yuan, P. Luo, W. Liu, and X. Wang, "ByteTrack: Multi-Object Tracking by Associating Every Detection Box," in European Conference on Computer Vision (ECCV), 2022. 4. DeepSORT: Real Time & Multi-Object Detection and Tracking with YOLO and TensorFlow A. Pujara and M. Bhamare, "DeepSORT: Real Time & Multi-Object Detection and Tracking with YOLO and TensorFlow," in 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), 2022, pp. 456–460. 5. Video Object Tracking Based on YOLOv7 and DeepSORT F. Yang, X. Zhang, and B. Liu, "Video Object Tracking Based on YOLOv7 and DeepSORT," arXiv preprint arXiv:2207.12202, 2022. 6. Multi-Object Tracking with Ultralytics YOLO Ultralytics, "Multi-Object Tracking with Ultralytics YOLO," 2023
Publication Date
7/24/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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