The iWildCam 2020 Competition Dataset
arXiv:2004.10340
Abstract
Camera traps enable the automatic collection of large quantities of image data. Biologists all over the world use camera traps to monitor animal populations. We have recently been making strides towards automatic species classification in camera trap images. However, as we try to expand the geographic scope of these models we are faced with an interesting question: how do we train models that perform well on new (unseen during training) camera trap locations? Can we leverage data from other modalities, such as citizen science data and remote sensing data? In order to tackle this problem, we have prepared a challenge where the training data and test data are from different cameras spread across the globe. For each camera, we provide a series of remote sensing imagery that is tied to the location of the camera. We also provide citizen science imagery from the set of species seen in our data. The challenge is to correctly classify species in the test camera traps.
arXiv commentFine-Grained Visual Categorization Workshop at CVPR 2020
References in corpus (6)
- WILDS: A Benchmark of in-the-Wild Distribution Shifts
- Efficient Pipeline for Camera Trap Image Review
- Perspectives on individual animal identification from biology and computer vision
- Camera-trap images segmentation using multi-layer robust principal component analysis
- The iWildCam 2018 Challenge Dataset
- The iWildCam 2021 Competition Dataset