Abstract
Due to its invisibility, NIR (Near-infrared) flash has been widely used to capture the images of wild animals in the night. Although the animals can be captured without notice, the gray NIR images are short of color and texture information and thus is difficult to analyze, for both human and machine. In this paper, we propose to use CycleGAN (Generative Adversarial Networks) to translate NIR image to the incandescent domain for visual quality enhancement. Example translations show that both color and texture can be well recovered by the proposed CycleGAN model. The recognition performance of a SSD based detector on the translated incandescent images is also significantly better than that on the original NIR images. Taking Wildebeest and Zebra for example, an increase of \(16\%\) and \(8\%\) in recognition accuracy has been observed.
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References
Arjovsky, M., Bottou, L.: Towards Principled Methods for Training Generative Adversarial Networks, January 2017. http://arxiv.org/abs/1701.04862
Burghardt, T., Calic, J., Thomas, B.T.: Tracking animals in wildlife videos using face detection. In: EWIMT (2004)
Chen, G., Han, T.X., He, Z., Kays, R., Forrester, T.: Deep convolutional neural network based species recognition for wild animal monitoring. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 858–862, October 2014. https://doi.org/10.1109/ICIP.2014.7025172
Gomez, A., Diez, G., Salazar, A., Diaz, A.: Animal identification in low quality camera-trap images using very deep convolutional neural networks and confidence thresholds. In: Bebis, G., et al. (eds.) ISVC 2016. LNCS, vol. 10072, pp. 747–756. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50835-1_67
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems 27 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition, pp. 770–778, June 2016. https://doi.org/10.1109/CVPR.2016.90
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, NIPS 2012, pp. 1097–1105. Curran Associates Inc., USA (2012). http://dl.acm.org/citation.cfm?id=2999134.2999257
LeCun, Y., Bottou, L., Bengio, Y., Patrick, H.: Gradient-based learning applied to document recognition. Proc. IEEE 45 (1998). https://doi.org/10.1007/BF00774006
Mirza, M., Osindero, S.: Conditional Generative Adversarial Nets. arXiv:1411.1784 (2014)
Norouzzadeh, M.S., et al.: Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Natl. Acad. Sci. 115(25), E5716–E5725 (2018). https://doi.org/10.1073/pnas.1719367115. https://www.pnas.org/content/115/25/E5716
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional GANs. In: International Conference on Learning Representations (2016). https://doi.org/10.1051/0004-6361/201527329
Ramanan, D., Forsyth, D.A., Barnard, K.: Detecting, localizing and recovering kinematics of textured animals. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005) - Volume 2 - Volume 02, CVPR 2005, pp. 635–642. IEEE Computer Society, Washington, D.C. (2005). https://doi.org/10.1109/CVPR.2005.126
Schneider, S., Taylor, G.W., Kremer, S.: Deep learning object detection methods for ecological camera trap data, pp. 321–328, May 2018. https://doi.org/10.1109/CRV.2018.00052
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556, September 2014
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 2, 150026 EP (2015). https://doi.org/10.1038/sdata.2015.26, Data Descriptor
Swinnen, K., Reijniers, J., Breno, M., Leirs, H.: A novel method to reduce time investment when processing videos from camera trap studies. PLoS ONE 9, e98881 (2014). https://doi.org/10.1371/journal.pone.0098881
Szegedy, C., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9, June 2015. https://doi.org/10.1109/CVPR.2015.7298594
Villa, A.G., Salazar, A., Vargas, F.: Towards automatic wild animal monitoring: identification of animal species in camera-trap images using very deep convolutional neural networks. Ecol. Inform. 41, 24–32 (2017). https://doi.org/10.1016/j.ecoinf.2017.07.004. https://www.sciencedirect.com/science/article/pii/S1574954116302047
Yu, X., Wang, J., Kays, R., Jansen, P.A., Wang, T., Huang, T.: Automated identification of animal species in camera trap images. EURASIP J. Image Video Process. 2013(1), 1–10 (2013). https://doi.org/10.1186/1687-5281-2013-52
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, October, pp. 2242–2251 (2017). https://doi.org/10.1109/ICCV.2017.244
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Gao, R., Zheng, S., He, J., Shen, L. (2020). CycleGAN-Based Image Translation for Near-Infrared Camera-Trap Image Recognition. In: Lu, Y., Vincent, N., Yuen, P.C., Zheng, WS., Cheriet, F., Suen, C.Y. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2020. Lecture Notes in Computer Science(), vol 12068. Springer, Cham. https://doi.org/10.1007/978-3-030-59830-3_39
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