@inproceedings{Brust2017AVM,
    type = {inproceedings},
    key = {Brust2017AVM},
    title = {Towards Automated Visual Monitoring of Individual Gorillas in the Wild},
    author = {Brust, Clemens-Alexander and Burghardt, Tilo and Groenenberg, Milou and Käding, Christoph and Kühl, Hjalmar and Manguette, Marie and Denzler, Joachim},
    booktitle = {ICCV Workshop on Visual Wildlife Monitoring (ICCV-WS)},
    year = {2017},
    pages = {2820-2830},
    abstract = {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.},
    doi = {10.1109/ICCVW.2017.333},
    groups = {finegrained,biodiversity},
}