The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Deep convolutional neural network based species recognition for wild animal monitoring
Abstract
We proposed a novel deep convolutional neural network based species recognition algorithm for wild animal classification on very challenging camera-trap imagery data. The imagery data were captured with motion triggered camera trap and were segmented automatically using the state of the art graph-cut algorithm. The moving foreground is selected as the region of interests and is fed to the proposed species recognition algorithm. For the comparison purpose, we use the traditional bag of visual words model as the baseline species recognition algorithm. It is clear that the proposed deep convolutional neural network based species recognition achieves superior performance. To our best knowledge, this is the first attempt to the fully automatic computer vision based species recognition on the real camera-trap images. We also collected and annotated a standard camera-trap dataset of 20 species common in North America, which contains 14, 346 training images and 9, 530 testing images, and is available to public for evaluation and benchmark purpose.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Learning Hierarchical Features for Scene Labeling
Farabet, C., Couprie, C., Najman, L., & LeCun, Y. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.231 · Cited by 2,620
Cited by
Norouzzadeh, M. S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M. S., Packer, C., & Clune, J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1719367115 · Cited by 1,230
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
Gu, T., Dolan-Gavitt, B., & Garg, S. (2017) · arXiv · doi:10.48550/arxiv.1708.06733 · Cited by 1,061
Recognition in Terra Incognita
Beery, S., Van Horn, G., & Perona, P. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01270-0_28 · Cited by 679
Machine learning to classify animal species in camera trap images: Applications in ecology
Tabak, M. A., Norouzzadeh, M. S., Wolfson, D. W., Sweeney, S. J., Vercauteren, K. C., Snow, N. P., Halseth, J. M., Di Salvo, P. A., Lewis, J. S., White, M. D., et al. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13120 · Cited by 504
A computer vision for animal ecology
Weinstein, B. G. (2018) · Journal of Animal Ecology · doi:10.1111/1365-2656.12780 · Cited by 429
Metadata
- Authors
- Chen, G., Han, T. X., He, Z., Kays, R., & Forrester, T.
- Venue
- 2014 IEEE International Conference on Image Processing (ICIP)
- Publisher
- IEEE
- Published
- 2014-10
- Pages
- 858-862
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W1982025193
- Cited by
- 183
- References
- 3
- FWCI
- 2.88
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Chen, G., Han, T. X., He, Z., Kays, R., & Forrester, T. (2014). Deep convolutional neural network based species recognition for wild animal monitoring. 2014 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip.2014.7025172
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