  We propose the first multi-frame video object detection framework trained to
detect great apes. It is applicable to challenging camera trap footage in
complex jungle environments and extends a traditional feature pyramid
architecture by adding self-attention driven feature blending in both the
spatial as well as the temporal domain. We demonstrate that this extension can
detect distinctive species appearance and motion signatures despite significant
partial occlusion. We evaluate the framework using 500 camera trap videos of
great apes from the Pan African Programme containing 180K frames, which we
manually annotated with accurate per-frame animal bounding boxes. These clips
contain significant partial occlusions, challenging lighting, dynamic
backgrounds, and natural camouflage effects. We show that our approach performs
highly robustly and significantly outperforms frame-based detectors. We also
perform detailed ablation studies and validation on the full ILSVRC 2015 VID
data corpus to demonstrate wider applicability at adequate performance levels.
We conclude that the framework is ready to assist human camera trap inspection
efforts. We publish code, weights, and ground truth annotations with this
paper.
