We propose the first metric learning system for the recognition of great ape
behavioural actions. Our proposed triple stream embedding architecture works on
camera trap videos taken directly in the wild and demonstrates that the
utilisation of an explicit DensePose-C chimpanzee body part segmentation stream
effectively complements traditional RGB appearance and optical flow streams. We
evaluate system variants with different feature fusion techniques and long-tail
recognition approaches. Results and ablations show performance improvements of
~12% in top-1 accuracy over previous results achieved on the PanAf-500 dataset
containing 180,000 manually annotated frames across nine behavioural actions.
Furthermore, we provide a qualitative analysis of our findings and augment the
metric learning system with long-tail recognition techniques showing that
average per class accuracy -- critical in the domain -- can be improved by ~23%
compared to the literature on that dataset. Finally, since our embedding spaces
are constructed as metric, we provide first data-driven visualisations of the
great ape behavioural action spaces revealing emerging geometry and topology.
We hope that the work sparks further interest in this vital application area of
computer vision for the benefit of endangered great apes.

通过使用稠密姿态 - C 黑猩猩身体部位分割流，本文首次提出了度量学习系统用于识别大型人猿的行为动作，并在 PanAf-500 数据集上取得了 12% 的性能提升，通过长尾识别技术，平均每类准确性可提高 23%。