ARCHIVES
Research Article
Animal Detection Using Machine Learning
A. Lalitha1
S. Nivasraj2
C. Praveen Kumar3
Girish. M.V4
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.
Published Online: May-June 2022
Pages: 64-68
Cite this article
No DOIReferences
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
Related Articles
2022
A Review on Bamboo Reinforced Concrete Beam
2022
FARMERS AGRICULTURAL PORTAL
2022
Sentiment Analysis of Religious Tweets
2022
Enhancement of beam strength by using bamboo as reinforcement in place of steel bars
2022
A Review on Anomaly Detection using PYOD Package
2022
