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- Vol. 3 Issue 3
- Article
Animal Detection Using Machine Learning
Authors
1Assistant Professor/Sr.G, Department of Computer Science & Engineering, SRM Valliammai Engineering College, Tamil Nadu, India. 234UG Student, Department of Computer Science & Engineering, SRM Valliammai Engineering College, Tamil Nadu, India.
Article Information
- DOI
- No DOI
- Published In
- Volume 3, Issue 3 (2022)
- Published Online
- May-June 2022
- Pages
- 64-68
- Article Type
- Research Article
- Check for updates
- No DOI available for cross-reference
References
1.Jaskó, G., Giosan, I., & Nedevschi, S. (2017, September). Animal detection from traffic scenarios based on monocular color vision. In Intelligent Computer Communication and Processing (ICCP), 2017 13th IEEE International Conference on (pp. 363-368). IEEE.
2.Nguyen, H., Maclagan, S. J., Nguyen, T. D., Nguyen, T., Flemons, P., Andrews, K., ... & Phung, D. (2017, October). Animal recognition and identification with deep convolutional neural networks for automated wildlife monitoring. In Data Science and Advanced Analytics (DSAA), 2017 IEEE International Conference on (pp. 40-49). IEEE.
3.Parham, J., Stewart, C., Crall, J., Rubenstein, D., Holmberg, J., & Berger-Wolf, T. (2018, March). An Animal Detection Pipeline for
Identification. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1075- 1083). IEEE.
4.Xue, W., Jiang, T., & Shi, J. (2017, September). Animal intrusion detection based on convolutional neural network. InCommunications and Information Technologies (ISCIT), 2017 17th International Symposium on (pp. 1-5). IEEE.
5.Zhu, C., Li, T. H., & Li, G. (2017, October). Towards automatic wild animal detection in low quality camera-trap images using two-channeled perceiving residual pyramid networks. InComputer Vision Workshop (ICCVW), 2017 IEEE International Conference on (pp. 2860-2864). IEEE
2.Nguyen, H., Maclagan, S. J., Nguyen, T. D., Nguyen, T., Flemons, P., Andrews, K., ... & Phung, D. (2017, October). Animal recognition and identification with deep convolutional neural networks for automated wildlife monitoring. In Data Science and Advanced Analytics (DSAA), 2017 IEEE International Conference on (pp. 40-49). IEEE.
3.Parham, J., Stewart, C., Crall, J., Rubenstein, D., Holmberg, J., & Berger-Wolf, T. (2018, March). An Animal Detection Pipeline for
Identification. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1075- 1083). IEEE.
4.Xue, W., Jiang, T., & Shi, J. (2017, September). Animal intrusion detection based on convolutional neural network. InCommunications and Information Technologies (ISCIT), 2017 17th International Symposium on (pp. 1-5). IEEE.
5.Zhu, C., Li, T. H., & Li, G. (2017, October). Towards automatic wild animal detection in low quality camera-trap images using two-channeled perceiving residual pyramid networks. InComputer Vision Workshop (ICCVW), 2017 IEEE International Conference on (pp. 2860-2864). IEEE
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