  Camera Traps are extensively used to observe wildlife in their natural
habitat without disturbing the ecosystem. This could help in the early
detection of natural or human threats to animals, and help towards ecological
conservation. Currently, a massive number of such camera traps have been
deployed at various ecological conservation areas around the world, collecting
data for decades, thereby requiring automation to detect images containing
animals. Existing systems perform classification to detect if images contain
animals by considering a single image. However, due to challenging scenes with
animals camouflaged in their natural habitat, it sometimes becomes difficult to
identify the presence of animals from merely a single image. We hypothesize
that a short burst of images instead of a single image, assuming that the
animal moves, makes it much easier for a human as well as a machine to detect
the presence of animals. In this work, we explore a variety of approaches, and
measure the impact of using short image sequences (burst of 3 images) on
improving the camera trap image classification. We show that concatenating
masks containing sequence information and the images from the 3-image-burst
across channels, improves the ROC AUC by 20% on a test-set from unseen
camera-sites, as compared to an equivalent model that learns from a single
image.
