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All issues Volume 85 (2026) ITM Web Conf., 85 (2026) 01012 References
Open Access
Issue
ITM Web Conf.
Volume 85, 2026
Intelligent Systems for a Sustainable Future (ISSF 2026)
Article Number 01012
Number of page(s) 8
Section AI for Healthcare, Agriculture, Smart Society & Computer Vision
DOI https://doi.org/10.1051/itmconf/20268501012
Published online 09 April 2026
  1. R. Sukumar, The Asian Elephant: Ecology and Management. Cambridge, U.K.: Cambridge University Press, 1989. [Google Scholar]
  2. P. Fernando, M. Jayasinghe, S. Prasad, H. Hendavitharana, and C. Pastorini, Review of HumanElephant Conflict mitigation measures used in South Asia, Gajah, vol. 31, pp. 41–52, 2009. [Google Scholar]
  3. The article by L. J. Shaffer, M. Khadka, J. Van Den Hoek, and H. Naithani, Human-Elephant Conflict: A Review of Current Management Strategies, vol. 33, no. 4, p. 805–814, 2019. [Google Scholar]
  4. M. S. Norouzzadeh et al., "Deep learning to identify, count, and describe wild animals in camera-trap images automated and manually, Proceedings of the National Academy of Sciences (PNAS), vol. 115, no. 25, pp. E5716 -E5725, 2018. [Google Scholar]
  5. S. Beery, G. Van Horn, and P. Perona, Recognition in Terra Incognita, in Proc. European Conf. Computer Vision (ECCV), 2018, pp. 456473. [Google Scholar]
  6. S. Christin, E. Hervet, and N. Lecomte, Applications to Deep Learning to Ecology, Methods in Ecology and Evolution, vol. 10, no. 10, pp. 16321644, 2019. [Google Scholar]
  7. The next entry concerns the method of capturing elephants with cameras and sending warnings to people about the danger of being caught in the middle of the wild. [7] N. Sharma, A. Kumar, and S. Gupta, Vision-Based Elephant Detection to support Early Warning Systems, in Proc. IEEE Int. Conf. Signal Processing and Communications, 2021, pp. 1–6. [Google Scholar]
  8. C. S. Reddy, V. K. Sharma, and P. K. Singh, "Surveillance System based on AI and detecting the intrusion of the elephant using this system to track their movement in the forest area, 2022, vol. 192, p. 3565–3574. [Google Scholar]
  9. J. Donahue et al., Long-term recurrent convolutional networks, visual recognition and description, Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2015, pp. 2625–2634. [Google Scholar]
  10. K. Simonyan and A. Zisserman, Two-Stream Convolutional Networks to Action Recognition in Videos, in Advances in Neural Information Processing Systems (NeurIPS), 2014, pp. 568576. [Google Scholar]
  11. L. Wang et al., Temporal Segment networks of Action Recognition in Videos, in Proc. European Conf. Computer Vision (ECCV), 2016, pp. 20–36. [Google Scholar]
  12. J. Redmon et al., You Only Look Once: Unified, Real-Time Object Detection, Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779–788. [Google Scholar]
  13. A. Bochkovskiy, C. Y. Wang and H. Y. M. Liao, YOLOv4: Optimal Speed and Accuracy of Object Detection, arXiv preprint, arXiv:2004.10934, 2020. [Google Scholar]
  14. N. Wojke, A. Bewley, and D. Paulus, Simple Online and Realtime Tracking based on Deep Association Metric, in Proc. IEEE Int. Conf. Image Processing (ICIP) 2017, pp. 36453649. [Google Scholar]
  15. A. Mathis et al., DeepLabCut: Markerless Pose Estimation of User-Defined Parts of the Body using Deep Learning, Nature neuroscience, 2018, vol. 21, no. 9, pp. 12811289. [Google Scholar]
  16. M. Kabra, A. A. Robie, M. Rivera-Alba, S. Branson and K. Branson, Interactive Machine Learning to Automatic Annotation of Animal Behavior, Nature Methods, vol. 10 no.1 pp. 6467, 2013. [Google Scholar]
  17. Zhao, Li and Chen, Intelligent Video Surveillance to protect wildlife, IEEE Access, vol. 8, pp. 209785–209796, 2020. [Google Scholar]
  18. A. Kumar, R. Mehta, and S. Verma, Early Warning of Human Wildlife Conflict through IoT-Based System, Sensors, vol. 23, no. 7, pp. 118. [Google Scholar]

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