Wild Animal Detection System Using Deep Convolutional Neural Networks
Keywords:
Artificial Intelligence, Deep Convolutional Neural Networks, Raspberry PI, Retinex filtering, Softmax.Abstract
Animal detecting and monitoring has always been a challenging in research area. Most of the animal detecting and monitoring processes rely on commercial wild camera trap to take wild animal pictures which are triggered by some sort of sensor techniques. The taken images still need human to collect and get analysed with tremendous amount of effort. In a wild environment, the cost for deploying, collecting, analyzing is quite significant. In progress of AI technique, there are mature tools that can be used to analyse the collected images. It can be utilized to solve the wild animal detecting and monitoring problem using Deep Convolutional Neural Networks. The idea is simple to run AI on Raspberry Pi locally to detect a wild animal and then it verifies the ima)ges. Then it send a message) through GSM module with no need of internet connection and gives an ultrasonic buzzer sound to divert a wild animal. It is trying to propose an end –to-end solution which could potentially reduce the loss of humans, animals and capitals using animal detecting system using Deep convolutional Neural Networks.
References
- Fang, Y., Du, S., Abdoola, R., Djouani, K., & Richards, C. (2016). Motion based animal detection in aerial videos.Procedia Computer Science, 92, 13-17.
- Jasko, 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.
- Nguyen, H., Maclagan, S. J., Nguyen, T. D., Nguyen, T., Flemons, P., Andrews, K., ... & Phung, D. (2017, October). Animal recognition and identification with deep convolution neural networks for automated Animal monitoring. In Data Science and Advanced Analytics (DSAA), 2017 IEEE International Conference on (pp. 40-49). IEEE.
- Kumar. S., & Singh, S. K. (2016). Monitoring of pet animal in smart cities using animal biometrics. Future Generation Computer Systems.
- 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.
- Matuska, S., Hudec, R., Kamencay, P., Benco, M., &Zachariasova, M. (2014). Classification of wild animals based on SVM and local descriptors. AASRI Procedia, 9, 25-30.
- Villa, A. G., Salazar, A., & Vargas, F. (2017). Towards automatic wild animal monitoring: Identification of animal species in camera-trap images using very deep convolutional neural networks. Ecological informatics, 41, 24-32.
- Xue, C., Wang, P., Zhao, J., Xu, A., & Guan, F. (2017). Development and validation of a universal primer pair for the simultaneous detection of eight animal species. Food chemistry, 221, 790-796.
- 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.
- Zhang, T., Wiliem, A., Hemsony, G., & Lovell, B. C. (2015, April). Detecting kangaroos in the wild: the first step towards automated animal surveillance. In ICASSP (pp. 1961-1965).
- 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. In Computer Vision Workshop (ICCVW), 2017 IEEE International Conference on (pp. 2860-2864). IEEE.
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