Abstract
We present development and analysis of deep learning architectures for real life and real-time native Indian monkey (langur) detection. Our monkey detection work is motivated with the idea of developing an application which will assist farmers in protecting their crops from attack of native wild animals. Protecting destruction of crops from wild animals is very important for agrarian societies. We engineered pre-trained VGG19 convolution neural network (CNN) to detect live monkeys in images streamed in an image sequence generated from a USB webcam. We used transfer learning approach to custom train, YOLO, a much faster CNN architecture to detect monkeys. For training and analysis, we used the Google Open Images V4 dataset. We give details of how two significantly different architectures were customized for native monkey detection. The performance of the detectors developed is analysed in terms of time taken for detection and accuracy of detection. Custom trained architecture achieved high true positive rate of 97.19% and true negative rate of 95.32%, which are significantly higher and better than the pre-trained architecture. The developed system was successfully tried out on a 4GB RAM and Intel quadcore i3 laptop at Indroda National park.
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References
Parikh, A.: Risks in Agriculture Farmers’ Perspective. SpringerBriefs in Economics (2017)
Allan, K.U.K., O’Connell, F., Nichols, J.D.: Camera Traps in Animal Ecology: Methods and Analyses. Springer Science & Business Media (2010)
Silveiraab, L., JÃącomoab, A.T.A., Diniz-Filhoac, J.A.F.: Camera trap, line transect census and track surveys: a comparative evaluation. Biol. Conserv. 114, 351–355 (2003). https://doi.org/10.1016/S0006-3207(03)00063-6
Norouzzadeh, M.S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M.S., Packer, C., Clune, J.: Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Nat. Acad. Sci. 115(25), E5716–E5725 (2018). https://www.pnas.org/content/115/25/E5716
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)
Swanson, A., Kosmala, M., Lintott, C., Simpson, R., Smith, A., Packer, C.: Snapshot serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna. Sci. Data 150026 (2015). https://doi.org/10.1038/sdata.2015.26
Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. CoRR (2018). arXiv:1804.02767
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J.R.R., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Duerig, T., Ferrari, V.: The open images dataset V4: unified image classification, object detection, and visual relationship detection at scale. CoRR (2018). arXiv:1811.00982
Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: unified, real-time object detection. CoRR (2015). arXiv:1506.02640
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. CoRR (2016). arXiv:1612.08242
Redmon, J.: Darknet: open source neural networks in c (2013–2016). http://pjreddie.com/darknet/
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Kumar, P., Sengupta, K., Lee, A.: A comparative study of different color spaces for foreground and shadow detection for traffic monitoring system. In: Proceedings of the IEEE 5th International Conference on Intelligent Transportation Systems, pp. 100–105. IEEE (2002)
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Duerig, T., Ferrari, V.: The open images dataset v4: unified image classification, object detection, and visual relationship detection at scale (2018). arXiv:1811.00982
Krasin, I., Duerig, T., Alldrin, N., Ferrari, V., Abu-El-Haija, S., Kuznetsova, A., Rom, H., Uijlings, J., Popov, S., Kamali, S., Malloci, M., Pont-Tuset, J., Veit, A., Belongie, S., Gomes, V., Gupta, A., Sun, C., Chechik, G., Cai, D., Feng, Z., Narayanan, D., Murphy, K.: Openimages: A public dataset for large-scale multi-label and multi-class image classification (2017). Dataset available from https://storage.googleapis.com/openimages/web/index.html
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Kumar, P., Shingala, M. (2021). Native Monkey Detection Using Deep Convolution Neural Network. In: Hassanien, A., Bhatnagar, R., Darwish, A. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2020. Advances in Intelligent Systems and Computing, vol 1141. Springer, Singapore. https://doi.org/10.1007/978-981-15-3383-9_34
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DOI: https://doi.org/10.1007/978-981-15-3383-9_34
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