Beyond animal detection: a benchmark for detecting animal age group

Abstract

There are numerous practical applications for animal age estimation, such as age simulation, population structure estimation, wildlife conservation, biometrics, and more. Most of the current methods either concentrate solely on animal detection or animal age estimation. However, animal detection provides limited information, which is not adequate for more sophisticated computer vision tasks. On the other hand, existing methods for estimating animal age are based primarily on facial images, but capturing an accurate image of an animal's face in a wild environment is extremely difficult and impractical for animals without discriminative facial features. In this paper, we propose a novel task, called Animal Age Group Detection. This task is a combination of animal detection and animal age estimation, with the primary goal of obtaining more information beyond the location of the animal by also providing crude age information, which is simple to accomplish with computer vision methods in a variety of open-world applications. To facilitate the study of this challenging, we present a benchmark for Animal Age Group Detection (AAGD), which contains a diverse range of 22 animal types, comprising a total of 4,778 images. These images are well-annotated with axis-aligned bounding boxes and include information about the age groups of the animals. In addition, we establish a baseline detector for this task, called AG-YOLOF. The release of AAGD aims to facilitate future research on the identification of animal age groups and enhance awareness of this significant area of study.