Automatic Detection and Recognition of Individuals in Patterned Species
This work addresses the challenge of processing large volumes of camera trap images for wildlife monitoring, offering a non-invasive and scalable solution for ecologists and conservationists, though it is incremental as it builds on existing object detection and feature extraction methods.
The authors tackled the problem of automating wildlife monitoring by developing a framework for automatic detection and recognition of individuals in patterned species like tigers, zebras, and jaguars, achieving perfect detection results in tiger images and competitive or better recognition performance compared to state-of-the-art methods.
Visual animal biometrics is rapidly gaining popularity as it enables a non-invasive and cost-effective approach for wildlife monitoring applications. Widespread usage of camera traps has led to large volumes of collected images, making manual processing of visual content hard to manage. In this work, we develop a framework for automatic detection and recognition of individuals in different patterned species like tigers, zebras and jaguars. Most existing systems primarily rely on manual input for localizing the animal, which does not scale well to large datasets. In order to automate the detection process while retaining robustness to blur, partial occlusion, illumination and pose variations, we use the recently proposed Faster-RCNN object detection framework to efficiently detect animals in images. We further extract features from AlexNet of the animal's flank and train a logistic regression (or Linear SVM) classifier to recognize the individuals. We primarily test and evaluate our framework on a camera trap tiger image dataset that contains images that vary in overall image quality, animal pose, scale and lighting. We also evaluate our recognition system on zebra and jaguar images to show generalization to other patterned species. Our framework gives perfect detection results in camera trapped tiger images and a similar or better individual recognition performance when compared with state-of-the-art recognition techniques.