Animal identification in low quality camera-trap images using very deep convolutional neural networks and confidence thresholds

Abstract

Monitoring animals in the wild without disturbing them is possible using camera trapping framework. Automatic triggered cameras, which take a burst of images of animals in their habitat, produce great volumes of data, but often result in low image quality. This high volume data must be classified by a human expert. In this work a two step classification is proposed to get closer to an automatic and trustfully camera-trap classification system in low quality images. Very deep convolutional neural networks were used to distinguish images, firstly between birds and mammals, secondly between mammals sets. The method reached 97.5%97.5% and 90.35%90.35% in each task. An alleviation mode using a confidence threshold of automatic classification is proposed, allowing the system to reach 100%100% of performance traded with human work.