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Camera Traps are extensively used to observe wildlife in their naturalhabitat without disturbing the ecosystem. This could help in the earlydetection of natural or human threats to animals, and help towards ecologicalconservation. Currently, a massive number of such camera traps have beendeployed at various ecological conservation areas around the world, collectingdata for decades, thereby requiring automation to detect images containinganimals. Existing systems perform classification to detect if images containanimals by considering a single image. However, due to challenging scenes withanimals camouflaged in their natural habitat, it sometimes becomes difficult toidentify the presence of animals from merely a single image. We hypothesizethat a short burst of images instead of a single image, assuming that theanimal moves, makes it much easier for a human as well as a machine to detectthe presence of animals. In this work, we explore a variety of approaches, andmeasure the impact of using short image sequences (burst of 3 images) onimproving the camera trap image classification. We show that concatenatingmasks containing sequence information and the images from the 3-image-burstacross channels, improves the ROC AUC by 20% on a test-set from unseencamera-sites, as compared to an equivalent model that learns from a singleimage.

Sequence Information Channel Concatenation for Improving Camera Trap Image Burst Classification