Efficient Pipeline for Camera Trap Image Review Sara Beery Dan Morris Siyu Yang
[email protected] [email protected] [email protected] California Institute of Technology Microsoft AI for Earth Microsoft AI for Earth Pasadena, California Redmond, Washington Redmond, Washington Figure 1: Example results from our generic detector, on images from regions and/or species not seen during training. ABSTRACT Previous work has shown good results on automated species Biologists all over the world use camera traps to monitor biodi- classification in camera trap data [8], but further analysis has shown versity and wildlife population density. The computer vision com- that these results do not generalize to new cameras or new geo- munity has been making strides towards automating the species graphical regions [3]. Additionally, these models will fail to recog- classification challenge in camera traps [1, 2, 4–16], but it has proven nize any species they were not trained on. In theory, it is possible difficult to to apply models trained in one region to images collected to re-train an existing model in order to add missing species, but in in different geographic areas. In some cases, accuracy falls off cata- practice, this is quite difficult and requires just as much machine strophically in new region, due to both changes in background and learning expertise as training models from scratch. Consequently, the presence of previously-unseen species. We propose a pipeline very few organizations have successfully deployed machine learn- that takes advantage of a pre-trained general animal detector and ing tools for accelerating camera trap image annotation.