Count, Crop and Recognise: Fine-Grained Recognition in the Wild
This addresses fine-grained recognition in the wild for applications like wildlife monitoring, but it is incremental as it builds on existing methods with a new staging approach.
The paper tackles the problem of labeling all animal individuals in every video frame, even when faces are not visible, by introducing a 'Count, Crop and Recognise' multistage process that boosts performance, as demonstrated on a new chimpanzee dataset.
The goal of this paper is to label all the animal individuals present in every frame of a video. Unlike previous methods that have principally concentrated on labelling face tracks, we aim to label individuals even when their faces are not visible. We make the following contributions: (i) we introduce a 'Count, Crop and Recognise' (CCR) multistage recognition process for frame level labelling. The Count and Recognise stages involve specialised CNNs for the task, and we show that this simple staging gives a substantial boost in performance; (ii) we compare the recall using frame based labelling to both face and body track based labelling, and demonstrate the advantage of frame based with CCR for the specified goal; (iii) we introduce a new dataset for chimpanzee recognition in the wild; and (iv) we apply a high-granularity visualisation technique to further understand the learned CNN features for the recognition of chimpanzee individuals.