Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas)
This work addresses wildlife monitoring for conservationists, but it is incremental as it applies existing methods like HOG to a new dataset of sea turtle images.
The researchers tackled the problem of automated biometric identification of sea turtles by using motion-activated cameras to collect carapace images and testing image descriptors, achieving a 65% classification accuracy with Histogram of Oriented Gradients (HOG).
Passive biometric identification enables wildlife monitoring with minimal disturbance. Using a motion-activated camera placed at an elevated position and facing downwards, we collected images of sea turtle carapace, each belonging to one of sixteen Chelonia mydas juveniles. We then learned co-variant and robust image descriptors from these images, enabling indexing and retrieval. In this work, we presented several classification results of sea turtle carapaces using the learned image descriptors. We found that a template-based descriptor, i.e., Histogram of Oriented Gradients (HOG) performed exceedingly better during classification than keypoint-based descriptors. For our dataset, a high-dimensional descriptor is a must due to the minimal gradient and color information inside the carapace images. Using HOG, we obtained an average classification accuracy of 65%.