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

## TL;DR

The paper introduces a new algorithm for identifying and locating marine species in images using color index thresholding and SURF features, improving tracking efficiency.

## Contribution

A novel first-stage algorithm combining color index-based segmentation and SURF-based location methods for marine species photo-identification and tracking.

## Key findings

- The proposed color index-based thresholding method improves segmentation accuracy for marine species in images.
- The SURF-based supervised classifier effectively identifies marine animal locations with high precision.
- The algorithm processes images in 0.33 seconds on a standard PC, suitable for real-time tracking.

## Abstract

Scientific sighting images are acquired during the time marine species emerge to the sea surface and until they submerge again. During this time, marine biologists manually make and capture their observations related to the abundance, conservation, and behavior of different species. This is inefficient and error-prone in many analysis scenarios. Smart cameras are commonly used to acquire the marine animal image and then process this image using computer vision systems to develop different applications. In this paper, a novel algorithm for the first stage of marine species photo-identification and location methods is presented. A color index-based thresholding method is proposed as a segmentation method that can be used as the first stage in photo-identification applications, and a SURFs (Speeded-Up Robust Features)-based method is proposed to obtain the location of marine animals in the image, which can be used as the first stage in marine species tracking methods. Finally, the proposed scheme is simple, efficient, and feasible for photo-identification and tracking applications.

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 GMR (Green Minus Red) color index and the proposed empirical BMG (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 SURFs (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.