Video-based Red Panda Individual Identification by Adaptively Aggregating Discriminative Features.

Existing animal individual identification methods mostly use only single images and cannot effectively leverage complementary features in video frames. To further improve the robustness and accuracy of individual identification of animals like red pandas that have complex body deformation or pose variations, we propose in this paper a deep network to learn hybrid feature representation of red pandas that adaptively aggregates local and global features for red panda identification. The local feature representation is obtained by adaptively finding discriminative local patches of the red panda in each frame and aggregating the local features across frames via a hypergraph neural network. The global feature representation is obtained by aggregating the features of different frames via average pooling. Red panda individuals are finally identified based on the concatenated local and global feature representations. Evaluation experiments have been done on a self-collected dataset of red panda videos. The results prove the utility of video data in animal individual identification as well as the superiority of our proposed method in exploiting discriminative features in video frames for identifying individual animals.