Identifying animals from a large group of possible individuals is very important for biodiversity monitoring and especially for collecting data on a small number of particularly interesting individuals, as these have to be identified first before this can be done. Identifying them can be a very time-consuming task. This is especially true, if the animals look very similar and have only a small number of distinctive features, like elephants do. In most cases the animals stay at one place only for a short period of time during which the animal needs to be identified for knowing whether it is important to collect new data on it. For this reason, a system supporting the researchers in identifying elephants to speed up this process would be of great benefit. In this paper, we present such a system for identifying elephants in the face of a large number of individuals with only few training images per individual. For that purpose, we combine object part localization, off-the-shelf CNN features, and support vector machine classification to provide field researches with proposals of possible individuals given new images of an elephant. The performance of our system is demonstrated on a dataset comprising a total of 2078 images of 276 individual elephants, where we achieve 56 top-1 test accuracy and 80 top-10 accuracy. To deal with occlusion, varying viewpoints, and different poses present in the dataset, we furthermore enable the analysts to provide the system with multiple images of the same elephant to be identified and aggregate confidence values generated by the classifier. With that, our system achieves a top-1 accuracy of 74 and a top-10 accuracy of 88 on the held-out test dataset.
Brust al have recently taken this approach to the deep learning age for gorilla identification using pre-trained convolutional neural networks (CNNs) for face detection and feature extraction @cite_11 . Their approach is, in principle, very similar to ours. However, we do not only demonstrate that it is also suitable for identifying other species such as elephants, but also show that the performance can be improved further by using earlier layers than the last layer of a CNN for feature extraction and additional pooling. Moreover, we found that simple data augmentation such as flipping can be useful for training the SVM classifier and show how to aggregate predictions obtained for multiple images of the same unknown individual to deal with occlusion and variations in pose and perspective.
@cite_11
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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 field photography in the wild. This effort aligns with a growing need for effective and integrated monitoring approaches for assessing the status of biodiversity at high spatio-temporal scales. Manual field photography and the utilisation of autonomous camera traps have already transformed the way ecological surveys are conducted. In principle, many environments can now be monitored continuously, and with a higher spatio-temporal resolution than ever before. Yet, the manual effort required to process photographic data to derive relevant information delimits any large scale application of this methodology. The described system applies existing computer vision techniques including deep convolutional neural networks to cover the tasks of detection and localisation, as well as individual identification of gorillas in a practically relevant setup. We evaluate the approach on a relatively large and challenging data corpus of 12,765 field images of 147 individual gorillas with image-level labels (i.e. missing bounding boxes) photographed at Mbeli Bai at the Nouabal-Ndoki National Park, Republic of Congo. Results indicate a facial detection rate of 90.8 AP and an individual identification accuracy for ranking within the Top 5 set of 80.3 . We conclude that, whilst keeping the human in the loop is critical, this result is practically relevant as it exemplifies model transferability and has the potential to assist manual identification efforts. We argue further that there is significant need towards integrating computer vision deeper into ecological sampling methodologies and field practice to move the discipline forward and open up new research horizons.