Towards Automated Visual Monitoring of Individual Gorillas in the Wild

CA Brust, Tilo Burghardt, M Groenenberg, C Kaeding, HS Kuehl, M Manguette, J Denzler

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

26 Citations (Scopus)
274 Downloads (Pure)


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.
Original languageEnglish
Title of host publication2017 IEEE International Conference of Computer Vision Workshop (ICCVW 2017)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages11
ISBN (Electronic)9781538610343
ISBN (Print)9781538610350
Publication statusPublished - Feb 2018
EventInternational Conference of Computer Vision 2017: VWM Workshop - Venice, Italy
Duration: 22 Oct 2017 → …

Publication series

ISSN (Print)2473-9944


ConferenceInternational Conference of Computer Vision 2017
Period22/10/17 → …


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