Rovero, F., Kays, R. Camera trapping for conservation. In Conservation Technology, chap. 10 (eds. Wich, S. & Piel, A. K.) 79–101 (Oxford University Press, 2021).
Boitani, L. Camera Trapping for Wildlife Research (Pelagic Publishing Ltd., 2016).
Rovero, F., Tobler, M. & Sanderson, J. Camera trapping for inventorying terrestrial vertebrates. Manual on field recording techniques and protocols for all taxa biodiversity inventories and monitoring. Belgian Natl. Focal Point Glob. Taxon. Initiat. 8, 100–128 (2010).
Norouzzadeh, M. S. et al. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Natl. Acad. Sci. 115, E5716–E5725. https://doi.org/10.1073/pnas.1719367115 (2018).
Niedballa, J., Sollmann, R., Courtiol, A. & Wilting, A. camtrapr: an r package for efficient camera trap data management. Methods Ecol. Evol. 7, 1457–1462 (2016).
Young, S., Rode-Margono, J. & Amin, R. Software to facilitate and streamline camera trap data management: A review. Ecol. Evol. 8, 9947–9957 (2018).
Vélez, J. et al. An evaluation of platforms for processing camera-trap data using artificial intelligence. Methods Ecol. Evol. 14, 459–477 (2023).
Hendry, H., Mann, C. Camelot–intuitive software for camera trap data management. BioRxiv 203216 (2017).
Chalmers, C., Fergus, P., Wich, S., Montanez, A.C. Conservation ai: Live stream analysis for the detection of endangered species using convolutional neural networks and drone technology. arXiv preprint arXiv:1910.07360 (2019).
Tabak, M. A. et al. Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: Mlwic2. Ecol. Evol. 10, 10374–10383 (2020).
Fennell, M., Beirne, C. & Burton, A. C. Use of object detection in camera trap image identification: Assessing a method to rapidly and accurately classify human and animal detections for research and application in recreation ecology. Glob. Ecol. Conserv. 35, e02104 (2022).
Peng, J. et al. Wild animal survey using uas imagery and deep learning: modified faster r-cnn for kiang detection in tibetan plateau. ISPRS J. Photogramm. Remote. Sens. 169, 364–376 (2020).
Zhu, H., Tian, Y. & Zhang, J. Class incremental learning for wildlife biodiversity monitoring in camera trap images. Eco. Inform. 71, 101760 (2022).
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. Eco. Inform. 41, 24–32 (2017).
Beery, S., Morris, D. & Yang, S. Efficient pipeline for camera trap image review. 1907, 06772 (2019).
Jocher, G. et al. ultralytics/yolov5: v3.0. Zenodo (2020).
Hughey, L. F., Hein, A. M., Strandburg-Peshkin, A. & Jensen, F. H. Challenges and solutions for studying collective animal behaviour in the wild. Philos. Trans. R. Soc. B Biol. Sci. 373, 20170005 (2018).
Wu, X., Sahoo, D. & Hoi, S. C. Recent advances in deep learning for object detection. Neurocomputing 396, 39–64 (2020).
Tong, K., Wu, Y. & Zhou, F. Recent advances in small object detection based on deep learning: A review. Image Vis. Comput. 97, 103910 (2020).
Guo, Y. et al. Varied channels region proposal and classification network for wildlife image classification under complex environment. IET Image Proc. 14, 585–591 (2020).
Gao, M., Du, Y., Yang, Y. & Zhang, J. Adaptive anchor box mechanism to improve the accuracy in the object detection system. Multimed. Tools Appl. 78, 27383–27402 (2019).
Miao, Z. et al. Insights and approaches using deep learning to classify wildlife. Sci. Rep. 9(1), 1–9 (2019).
Zhao, Z.-Q., Zheng, P., Xu, S.-T. & Wu, X. Object detection with deep learning: A review. IEEE Trans. Neural Netw. Learn. Syst. 30, 3212–3232 (2019).
Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P. Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision, 2980–2988 (2017).
Liu, W. et al. Ssd: Single shot multibox detector. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, 21–37 (Springer, 2016).
Yoo, D., Park, S., Lee, J.-Y., Paek, A.S., So Kweon, I. Attentionnet: Aggregating weak directions for accurate object detection. In Proceedings of the IEEE International Conference on Computer Vision, 2659–2667 (2015).
Girshick, R., Donahue, J., Darrell, T., Malik, J. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 580–587 (2014).
Ren, S., He, K., Girshick, R., Sun, J. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 28 (2015).
Dai, J., Li, Y., He, K., Sun, J. R-fcn: Object detection via region-based fully convolutional networks. Adv. Neural Inf. Process. Syst. 29 (2016).
Janzen, M., Ritter, A., Walker, P. D. & Visscher, D. R. Eventfinder: a program for screening remotely captured images. Environ. Monit. Assess. 191, 1–10 (2019).
Falzon, G. et al. Classifyme: a field-scouting software for the identification of wildlife in camera trap images. Animals 10, 58 (2019).
Parikh, M., Patel, M. & Bhatt, D. Animal detection using template matching algorithm. Int. J. Res. Mod. Eng. Emerg. Technol 1, 26–32 (2013).
Swinnen, K. R., 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).
Antônio, W. H., Da Silva, M., Miani, R. S. & Souza, J. R. A proposal of an animal detection system using machine learning. Appl. Artif. Intell. 33, 1093–1106 (2019).
Yu, X. et al. Automated identification of animal species in camera trap images. EURASIP J. Image Video Process. 2013, 1–10 (2013).
Yousif, H., Yuan, J., Kays, R., He, Z. Fast human-animal detection from highly cluttered camera-trap images using joint background modeling and deep learning classification. In 2017 IEEE International Symposium on Circuits and Systems (ISCAS), 1–4 (IEEE, 2017).
Verma, G.K., Gupta, P. Wild animal detection using deep convolutional neural network. In Proceedings of 2nd International Conference on Computer Vision & Image Processing: CVIP 2017, vol. 2, 327–338 (Springer, 2018).
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), 858–862 (IEEE, 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, E5716–E5725 (2018).
Zualkernan, I. et al. An iot system using deep learning to classify camera trap images on the edge. Computers 11, 13 (2022).
Zhao, B., Feng, J., Wu, X. & Yan, S. A survey on deep learning-based fine-grained object classification and semantic segmentation. Int. J. Autom. Comput. 14, 119–135 (2017).
Zett, T., Stratford, K. J. & Weise, F. J. Inter-observer variance and agreement of wildlife information extracted from camera trap images. Biodivers. Conserv. 31, 3019–3037 (2022).
Korsch, D., Bodesheim, P., Denzler, J. Classification-specific parts for improving fine-grained visual categorization. In Pattern Recognition: 41st DAGM German Conference, DAGM GCPR 2019, Dortmund, Germany, September 10–13, 2019, Proceedings 41, 62–75 (Springer, 2019).
Cui, Y., Song, Y., Sun, C., Howard, A., Belongie, S. Large scale fine-grained categorization and domain-specific transfer learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4109–4118 (2018).
Rodner, E. et al. Fine-grained recognition datasets for biodiversity analysis. 1507, 00913 (2015).
Gebru, T., Hoffman, J., Fei-Fei, L. Fine-grained recognition in the wild: A multi-task domain adaptation approach. In Proceedings of the IEEE International Conference on Computer Vision, 1349–1358 (2017).
Rigoudy, N. et al. The deepfaune initiative: a collaborative effort towards the automatic identification of European fauna in camera trap images. Eur. J. Wildl. Res. 69, 113 (2023).
Gooliaff, T. & Hodges, K. E. Measuring agreement among experts in classifying camera images of similar species. Ecol. Evol. 8, 11009–11021 (2018).
Leorna, S., Brinkman, T. Human vs. machine: Detecting wildlife in camera trap images. Ecol. Inform. 72, 101876 (2022).
VÃlez, J. et al. Choosing an appropriate platform and workflow for processing camera trap data using artificial intelligence (2022). 2202.02283.
Szegedy, C. et al. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1–9 (2015).
Krizhevsky, A., Sutskever, I., Hinton, G.E. Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25 (2012).
He, K., Zhang, X., Ren, S., Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778 (2016).
Simonyan, K., Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).
Chollet, F. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1251–1258 (2017).
Lumini, A. & Nanni, L. Deep learning and transfer learning features for plankton classification. Eco. Inform. 51, 33–43 (2019).
Deng, J. et al. Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–255 (IEEE, 2009).