First-Stage Algorithm for Photo-Identification and Location of Marine Species

Marine species photo-identification and location for tracking are crucial for understanding the characteristics and patterns that distinguish each marine species. However, challenges in camera data acquisition and the unpredictability of animal movements have restricted progress in this field. To address these challenges, we present a novel algorithm for the first stage of marine species photo-identification and location methods. For marine species photo-identification applications, a color index-based thresholding segmentation method is proposed. This method is based on the characteristics of the <i>GMR</i> (Green Minus Red) color index and the proposed empirical <i>BMG</i> (Blue Minus Green) color index. These color indexes are modified to provide better information about the color of regions, such as marine animals, the sky, and land found in the scientific sightings images, allowing an optimal thresholding segmentation method. In the case of marine species location, a <i>SURFs</i> (Speeded-Up Robust Features)-based supervised classifier is used to obtain the location of the marine animal in the sighting image; with this, its tracking could be obtained. The tests were performed with the Kaggle happywhale public database; the results obtained in precision shown range from 0.77 up to 0.98 using the proposed indexes. Finally, the proposed method could be used in real-time marine species tracking with a processing time of 0.33 s for images of 645 × 376 pixels using a standard PC.