Individual Penguin Identification Research
Examples from the dataset: These RGB images created from the hyperspectral images.
Hyperspectral Image Dataset for Individual Penguin Identification
Remote individual animal identification is important for food safety, sport, and animal conservation. Numerous existing remote individual animal identification studies have focused on RGB images. In this paper, we tackle individual penguin identification using hyperspectral (HS) images. To the best of our knowledge, it is the first work to analyze spectral differences between penguin individuals using an HS camera. We have constructed a novel penguin HS image dataset, including 990 hyperspectral images of 27 penguins. We experimentally demonstrate that the spectral information of HS image pixels can be used for individual penguin identification. The experimental results show the effectiveness of using HS images for individual penguin identification.
Experimental procedures: (a) Pixels of the target penguins are selected from the captured hyperspectral (HS) images. (b) Pixels selected from the HS images retain information on the wavelength of reflected light. (c) A simple 5-layer MLP is used to predict individual labels of penguins. (d) Individual identification was performed using a single pixel.
Visualisation of classification results: Selection of input pixels with an application in mind and visualization of predicted labels.
Classification accuracy for different data types (%).
Appearance-and-Spectral-Based Identification System for Penguin Individuals
We present a system for identifying individual penguins in flock images by combining appearance-based penguin detection with RGB images and spectral-based penguin identification with hyperspectral (HS) information. For the spectral-based identification, we propose a classification and ensemble approach inspired by the observation that many animals have similar color patterns. For example, penguins typically have black and white coloration, with consistent color distributions across individuals. The proposed system's effectiveness was experimentally validated with a mean Average Precision (mAP) metric. The proposed Appearance-and-Spectral-Based identification system significantly outperformed conventional appearance-based and spectral-based systems.
Proposed system that effectively integrates appearance and spectral information. It uses RGB images for animal detection and spectral data for individual identification.
Visual comparison of penguin identification results across different approaches. Our method demonstrates superior performance in accurately identifying individual penguins across various image resolutions. For the Spectralbased approach, colored areas indicate predicted penguin locations: green for correct identifications and red for misidentifications.
