Cattle detection and counting in UAV images based on convolutional neural networks

References

• Agrawal, P., R. Girshick, and J. Malik. 2014. “Analyzing the Performance of Multilayer Neural Networks for Object Recognition.” In Computer Vision – ECCV 2014, edited by D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, 329–344. Cham: Springer International Publishing.
   Google Scholar
• Ardã, H., O. Guzhva, M. Nilsson, and A. H. Herlin. 2018. “Convolutional Neural Network-Based Cow Interaction Watchdog.” IET Computer Vision 12 (2): 171–177. doi:10.1049/iet-cvi.2017.0077.
   Web of Science ®Google Scholar
• Chamoso, P., W. Raveane, V. Parra, and G. Angélica. 2014. “UAVs Applied to the Counting and Monitoring of Animals.” In Ambient Intelligence - Software and Applications, edited by C. Ramos, P. Novais, C. E. Nihan, and J. M. Corchado Rodríguez, 71–80. Cham: Springer International Publishing.
   Google Scholar
• Cheng, Y., D. Wang, P. Zhou, and T. Zhang. 2018. “Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges.” IEEE Signal Processing Magazine 126–136. doi:10.1109/MSP.2017.2765695.
   Google Scholar
• Chrtien, L.-P., J. Thau, and P. Mnard. 2016. “Visible and Thermal Infrared Remote Sensing for the Detection of White-Tailed Deer Using an Unmanned Aerial System.” Wildlife Society Bulletin 40 (1): 181–191. https://onlinelibrary.wiley.com/doi/abs/10.1002/wsb.629
   Web of Science ®Google Scholar
• Cignoni, P., M. Callieri, M. Corsini, M. Dellepiane, F. Ganovelli, and G. Ranzuglia. 2008. “MeshLab: An Open-Source Mesh Processing Tool.” In Eurographics Italian Chapter Conference, edited by V. Scarano, R. De Chiara, and U. Erra. Eurographics Association. Salerno, Italy.
   Google Scholar
• Cortes, C., and V. Vapnik. 1995. “Support-Vector Networks.” Machine-Learning 20 (3): 273–297. doi:10.1007/BF00994018.
   Web of Science ®Google Scholar
• Dalal, N., and B. Triggs. 2005. “Histograms of Oriented Gradients for Human Detection.” In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), Vol. 1, June, 886–893. San Diego, CA, USA.
   Google Scholar
• Deng, J., W. Dong, R. Socher, L. Li, L. Kai, and L. Fei-Fei. 2009. “ImageNet: A Large-Scale Hierarchical Image Database.” In 2009 IEEE Conference on Computer Vision and Pattern Recognition, June, 248–255. Miami, FL, USA.
   Google Scholar
• Everingham, M., L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. 2012. “The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results.” http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html
   Google Scholar
• Fang, Y., D. Shengzhi, R. Abdoola, K. Djouani, and C. Richards. 2016. “Motion Based Animal Detection in Aerial Videos.” Procedia Computer Science 92: 13–17. 2nd International Conference on Intelligent Computing, Communication & Convergence, ICCC 2016, 24-25 January 2016, Bhubaneswar, Odisha, India http://www.sciencedirect.com/science/article/pii/S1877050916315629
   Google Scholar
• Furukawa, Y., B. Curless, S. M. Seitz, and R. Szeliski. 2010. “Towards Internet-Scale Multi-View Stereo.” In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June, 1434–1441.
   Google Scholar
• Furukawa, Y., and J. Ponce. 2010. “Accurate, Dense, and Robust Multiview Stereopsis.” IEEE Transactions on Pattern Analysis and Machine Intelligence 32 (8): 1362–1376. doi:10.1109/TPAMI.2009.161.
   PubMed Web of Science ®Google Scholar
• Gonzalez, L. F., G. A. Montes, E. Puig, S. Johnson, K. Mengersen, and K. J. Gaston. 2016. “Unmanned Aerial Vehicles (Uavs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation.” Sensors 16: 1. doi:10.3390/s16122100.
   Web of Science ®Google Scholar
• Han, S., H. Mao, and W. J. Dally. 2016. “Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.” In International Conference on Learning Representations, San Juan, Puerto Rico.
   Google Scholar
• He, K., X. Zhang, S. Ren, and J. Sun. 2016. “Deep Residual Learning for Image Recognition.” In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, 770–778. Las Vegas, NV, USA.
   Google Scholar
• He, K., R. B. Girshick, and D. Piotr 2018. “Rethinking ImageNet Pre-Training.” CoRR abs/1811.08883. http://arxiv.org/abs/1811.08883
   Google Scholar
• Hinton, G., O. Vinyals, and J. Dean. 2014. “Distilling the Knowledge in a Neural Network.” Deep Learning and Representation Learning Workshop in Conjunction with NIPS, Montreal, Canada.
   Google Scholar
• Hodgson, J. C., S. M. Rowan Mott, T. T. Baylis, S. W. Pham, A. D. Kilpatrick, R. R. Segaran, I. Reid, A. Terauds, and L. P. Koh. 2018. “Drones Count Wildlife More Accurately and Precisely than Humans.” Methods in Ecology and Evolution 9 (5): 1160–1167. https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.12974
   Web of Science ®Google Scholar
• Kazhdan, M., and H. Hoppe. 2013. “Screened Poisson Surface Reconstruction.” ACM Transactions on Graphics 32 (3): 29:1–29: 13. doi:10.1145/2487228.2487237.
   PubMed Web of Science ®Google Scholar
• Kellenberger, B., M. Volpi, and D. Tuia. 2017. “Fast Animal Detection in UAV Images Using Convolutional Neural Networks.” In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July, 866–869. Fort Worth, Texas, USA.
   Google Scholar
• Kellenberger, B., D. Marcos, and D. Tuia. 2018. “Detecting Mammals in UAV Images: Best Practices to Address a Substantially Imbalanced Dataset with Deep Learning.” Remote Sensing of Environment 216: 139–153. doi:10.1016/j.rse.2018.06.028.
   Web of Science ®Google Scholar
• Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2012. “ImageNet Classification with Deep Convolutional Neural Networks.” In Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, NIPS’12, USA, 1097–1105. Curran Associates . http://dl.acm.org/citation.cfm?id=2999134.2999257
   Google Scholar
• Lhoest, S., J. Linchant, S. Quevauvillers, C. Vermeulen, and P. Lejeune. 2015. “How Many Hippos (HOMHIP): Algorithm for Automatic Counts of Animals with Infra-Red Thermal Imagery from UAV.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W3: 335–362.
   Google Scholar
• Lin, T.-Y., S. J. Michael Maire, L. D. Belongie, R. B. Bourdev, J. H. Girshick, P. Perona, D. Ramanan, P. Dollár, and C. Lawrence Zitnick. 2014. “Microsoft COCO: Common Objects in Context.” In European Conference on Computer Vision, 740–755. Zurich, Switzerland.
   Google Scholar
• Liu, W., D. Anguelov, D. Erhan, C. Szegedy, S. Reed, F. Cheng-Yang, and A. C. Berg. 2016. “SSD: Single Shot MultiBox Detector.” In Computer Vision – ECCV 2016, edited by B. Leibe, J. Matas, N. Sebe, and M. Welling, 21–37. Cham: Springer International Publishing.
   Google Scholar
• Liu, X., S. W. Chen, S. Aditya, N. Sivakumar, S. Dcunha, Q. Chao, C. J. Taylor, J. Das, and V. Kumar. 2018. “Robust Fruit Counting: Combining Deep Learning, Tracking, and Structure from Motion.” In IEEE/RSJ International Conference on Intelligent Robots and Systems, Madrid, Spain.
   Google Scholar
• Longmore, S. N., R. P. Collins, S. Pfeifer, S. E. Fox, M. Mulero-Pzmny, F. Bezombes, A. Goodwin, M. De Juan Ovelar, J. H. Knapen, and S. A. Wich. 2017. “Adapting Astronomical Source Detection Software to Help Detect Animals in Thermal Images Obtained by Unmanned Aerial Systems.” International Journal of Remote Sensing 38 (8–10): 2623–2638. doi:10.1080/01431161.2017.1280639.
   Web of Science ®Google Scholar
• Lowe, D. G. 2004. “Distinctive Image Features from Scale-Invariant Keypoints.” International Journal of Computer Vision 60 (2): 91–110. doi:10.1023/B:VISI.0000029664.99615.94.
   Web of Science ®Google Scholar
• Marcos, D., B. Kellenberger, S. Lobry, and D. Tuia. 2018. “Scale Equivariance in CNNs with Vector Fields.” In ICML/FAIM 2018 workshop on Towards learning with limited labels: Equivariance, Invariance, and Beyond, Stockholm, Sweden.
   Google Scholar
• Miller, M. L., H. S. Stone, and I. J. Cox. 1997. “Optimizing Murty’s Ranked Assignment Method.” IEEE Transactions on Aerospace and Electronic Systems 33 (3): 851–862. doi:10.1109/7.599256.
   Web of Science ®Google Scholar
• Mundhenk, T., G. K. Nathan, W. A. Sakla, and K. Boakye. 2016. “A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning.” In Computer Vision – ECCV 2016, edited by B. Leibe, J. Matas, N. Sebe, and M. Welling, 785–800. Cham: Springer International Publishing.
   Google Scholar
• Munkres, J. 1957. “Algorithms for the Assignment and Transportation Problems.” Journal of the Society for Industrial and Applied Mathematics 5 (1): 32–38. doi:10.1137/0105003.
   Google Scholar
• Oishi, Y., and T. Matsunaga. 2014. “Support System for Surveying Moving Wild Animals in the Snow Using Aerial Remote-Sensing Images.” International Journal of Remote Sensing 35 (4): 1374–1394. doi:10.1080/01431161.2013.876516.
   Web of Science ®Google Scholar
• Razavian, A. S., H. Azizpour, J. Sullivan, and S. Carlsson. 2014. “CNN Features Off-the-Shelf: An Astounding Baseline for Recognition.” In Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW ‘14, 512–519, Washington, DC, USA: IEEE Computer Society. doi: 10.1109/CVPRW.2014.131.
   Google Scholar
• Redmon, J., and A. Farhadi. 2017. “YOLO9000: Better, Faster, Stronger.” In IEEE Conference on Computer Vision and Pattern Recognition, 6517–6525. Honolulu, Hawaii, USA. doi:10.1109/CVPR.2017.690.
   Google Scholar
• Redmon, J. 2013–2016. “Darknet: Open Source Neural Networks in C.” http://pjreddie.com/darknet/
   Google Scholar
• Ren, S., K. He, R. Girshick, and J. Sun. 2017. “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.” IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (6): 1137–1149. doi:10.1109/TPAMI.2016.2577031.
   PubMed Web of Science ®Google Scholar
• Rivas, A., P. Chamoso, A. Gonzlez-Briones, and J. M. Corchado. 2018. “Detection of Cattle Using Drones and Convolutional Neural Networks.” Sensors 18: 7. doi:10.3390/s18072048.
   Web of Science ®Google Scholar
• Salberg, A. 2015. “Detection of Seals in Remote Sensing Images Using Features Extracted from Deep Convolutional Neural Networks.” In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July, 1893–1896. Milan, Italy.
   Google Scholar
• Schönberger, J. L., and J. Frahm. 2016. “Structure-from-Motion Revisited.” In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, 4104–4113. Las Vegas, NV, USA.
   Google Scholar
• Segal, M., and K. Akeley. 2017. “The OpenGL Graphics System: A Specification, Version 4.5 (Core Profile).” https://www.khronos.org/registry/OpenGL/specs/gl/glspec45.core.pdf
   Google Scholar
• Tayara, H., K. Gil Soo, and K. T. Chong. 2018. “Vehicle Detection and Counting in High-Resolution Aerial Images Using Convolutional Regression Neural Network.” IEEE Access 6: 2220–2230. doi:10.1109/ACCESS.2017.2782260.
   Web of Science ®Google Scholar
• Van Gemert, J. C., C. R. Verschoor, P. Mettes, K. Epema, L. P. Koh, and S. Wich. 2015. “Nature Conservation Drones for Automatic Localization and Counting of Animals.” In Computer Vision - ECCV 2014 Workshops, edited by L. Agapito, M. M. Bronstein, and C. Rother, 255–270. Cham: Springer International Publishing.
   Google Scholar
• Wu, C. 2013. “Towards Linear-Time Incremental Structure from Motion.” In International Conference on 3D Vision - 3DV, June, 127–134. Seattle, Washington, USA.
   Google Scholar
• Wu, C., S. Agarwal, B. Curless, and S. M. Seitz. 2011. “Multicore Bundle Adjustment.” In IEEE Conference on Computer Vision and Pattern Recognition, June, 3057–3064. Colorado Springs, CO, USA.
   Google Scholar
• Xu, Y., T. Xiao, J. Zhang, K. Yang, and Z. Zhang. 2014. “Scale-Invariant Convolutional Neural Networks.” CoRR abs/1411.6369. http://arxiv.org/abs/1411.6369
   Google Scholar