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In this paper we report on the context and evaluation of a system for an automatic interpretation of sightings of individual western lowland gorillas (Gorilla gorilla gorilla) as captured in facial fieldVisual data acquisition in the field often captures sufficient information to establish encounters with individual gorillas. However, relevant information is locked within the pixel patterns measured, usually requiring expert knowledge and time-consuming efforts for identification. Computer vision can help to extract gorilla identities by performing automated species detection, followed by individual facial identification. We show that standard deep learning models combined with a traditional SVM classifier can be used for this task. To assist encounter processing, predictions can be presented graphically with known population information as shown.

The great success that deep models have achieved in the past is mainly owed to large amounts of labeled training data. However, the acquisition of labeled data for new tasks aside from existing benchmarks is both challenging and costly. Active learning can make the process of labeling new data more efficient by selecting unlabeled samples which, when labeled, are expected to improve the model the most. In this paper, we combine a novel method of active learning for object detection with an incremental learning scheme (Käding et al., 2016b) to enable continuous exploration of new unlabeled datasets. We propose a set of uncertaintybased active learning metrics suitable for most object detectors. Furthermore, we present an approach to leverage class imbalances during sample selection. All methods are evaluated systematically in a continuous exploration context on the PASCAL VOC 2012 dataset (Everingham et al., 2010).

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