Detecting Animals in Infrared Images from Camera-Traps

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.