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We propose the first multi-frame video object detection framework trained todetect great apes. It is applicable to challenging camera trap footage incomplex jungle environments and extends a traditional feature pyramidarchitecture by adding self-attention driven feature blending in both thespatial as well as the temporal domain. We demonstrate that this extension candetect distinctive species appearance and motion signatures despite significantpartial occlusion. We evaluate the framework using 500 camera trap videos ofgreat apes from the Pan African Programme containing 180K frames, which wemanually annotated with accurate per-frame animal bounding boxes. These clipscontain significant partial occlusions, challenging lighting, dynamicbackgrounds, and natural camouflage effects. We show that our approach performshighly robustly and significantly outperforms frame-based detectors. We alsoperform detailed ablation studies and validation on the full ILSVRC 2015 VIDdata corpus to demonstrate wider applicability at adequate performance levels.We conclude that the framework is ready to assist human camera trap inspectionefforts. We publish code, weights, and ground truth annotations with thispaper.

Great Ape Detection in Challenging Jungle Camera Trap Footage via Attention-Based Spatial and Temporal Feature Blending