Detecting Animals in Infrared Images from Camera-Traps
- 作者: Follmann P.1,2, Radig B.1
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隶属关系:
- Faculty of Informatics
- Research
- 期: 卷 28, 编号 4 (2018)
- 页面: 605-611
- 栏目: Proceedings of the 6th International Workshop
- URL: https://journals.rcsi.science/1054-6618/article/view/195455
- DOI: https://doi.org/10.1134/S1054661818040107
- ID: 195455
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详细
Camera traps mounted on highway bridges capture millions of images that allow investigating animal populations and their behavior. As the manual analysis of such an amount of data is not feasible, automatic systems are of high interest. We present two different of such approaches, one for automatic outlier classification, and another for the automatic detection of different objects and species within these images. Utilizing modern deep learning algorithms, we can dramatically reduce the engineering effort compared to a classical hand-crafted approach. The results achieved within one day of work are very promising and are easily reproducible, even without specific computer vision knowledge.
作者简介
P. Follmann
Faculty of Informatics; Research
编辑信件的主要联系方式.
Email: follmann@mvtec.com
德国, Munich; Munich
B. Radig
Faculty of Informatics
Email: follmann@mvtec.com
德国, Munich
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