Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67124
Volume:
10 |
9 |
Sep 2026
Pages:
1-4
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.
Department of Computer Science amd Engineering
Vidarbha Institute of Technology
Department of Computer Science amd Engineering
Vidarbha Institute of Technology
Human–wildlife encounters have become increasingly frequent in forest-fringe communities, agricultural regions, and rural settlements, creating significant risks for both humans and animals. This paper presents an AI-driven wildlife surveillance and threat detection system designed for the real-time identification of potentially dangerous animals, particularly tigers, leopards, and lions. The proposed system employs the YOLOv10 deep learning architecture to perform rapid object detection from surveillance video streams. In addition to animal identification, the system incorporates object tracking, automatic snapshot generation, timestamp recording, SQLite-based event logging, and email-based alert notification within a unified monitoring framework. A customized dataset obtained from the Roboflow Universe, containing approximately 2,000 tiger images, 700 lion images, and more than 500 leopard images, was utilized for model development. The YOLOv10s model was trained using the prepared dataset and subsequently integrated into a Python-based surveillance application. The resulting system provides automated wildlife detection and event monitoring, thereby reducing dependence on continuous manual observation. The proposed approach demonstrates the potential of deep-learning-based computer vision for real-time wildlife surveillance in forest-border areas, agricultural zones, and other remote locations.
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
9/7/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
11
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...