Tilo Burghardt, X Yang, F Sakib, M Mirmehdi
The problem of visually identifying the presence and locations of animal species filmed in natural habitats is of central importance for automating the interpretation of large-scale camera trap imagery. This is particularly challenging in scenarios where lighting is difficult, backgrounds are non-static, and major occlusions, image noise, as well as animal camouflage effects occur: filming great apes viacamera traps in jungle environments constitutes one such setting. Finding animals under these conditions and classifying their behaviours are important tasks in order to exploit the filmed material for conservation or biological modelling.
Together with researchers from various institutions including the Max Planck Institute for Evolutionary Anthropology we developed deep learning systems for detecting great apes in challenging imagery in the first place and for identifying animal behaviours exhibited in these camera trap clips once apes have been recognised.
F Sakib, T Burghardt. Visual Recognition of Great Ape Behaviours in the Wild. In press. Proc. 25th International Conference on Pattern Recognition (ICPR) Workshop on Visual Observation and Analysis of Vertebrate And Insect Behavior (VAIB), January 2021. (Arxiv PDF)
X Yang, M Mirmehdi, T Burghardt. Great Ape Detection in Challenging Jungle Camera Trap Footage via Attention-Based Spatial and Temporal Feature Blending. Computer Vision for Wildlife Conservation (CVWC) Workshop at IEEE International Conference of Computer Vision (ICCVW), pp. 255-262, October 2019. (DOI:10.1109/ICCVW.2019.00034), (CVF Version), (Arxiv PDF), (Dataset PanAfrican2019 Video), (Dataset PanAfrican2019 Annotations and Code)