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.

开发了一个自动检测和识别老虎、斑马和美洲豹等有斑点物种个体的框架，使用了 Faster-RCNN 目标检测框架来有效地在图像中检测动物，在动物的侧面提取 AlexNet 特征并训练逻辑回归 (或线性 SVM) 分类器来识别个体。在相机陷阱老虎图像数据集上测试和评估后发现，和最先进的识别技术相比，我们的框架在检测结果方面具有完美的表现，在个体的识别方面，表现相似或更好。