Camera traps are a non-invasive and time-efficient tool for collecting data on the activity and identity of wildlife at specific locations. A single researcher can easily sample continuously at dozens of locations simultaneously. Camera-trapping surveys , however, come with a new challenge, namely the processing of the enormous volumes of imagery that they may yield. Date, time and technical data can be pulled from the exif information stored in the images, but determining what the images often remains a burdening task.
One obvious solution is to use deep learning to detect and identify animals in images.
The challenge is thus to develop an algorithm for the fast and accurate classification of wildlife in camera-trap images from Hoge Veluwe National Park
You will be given a library of images for the eight most common species of NPHV. The images have already been separated into day and night frames, and into empty frames and frames with one or more animals.
You will be given access to a shared folder containing a couple dozen peer-reviewed articles on image recognition for camera trapping studies.