论文标题
开放型：带注释的APE照片的数据库进行姿势估计
OpenApePose: a database of annotated ape photographs for pose estimation
论文作者
论文摘要
由于它们与人类的密切关系，非人类猿（黑猩猩，bonobos，大猩猩，猩猩，猩猩和吉本斯，包括暹罗人）具有极大的科学兴趣。通过执行基于视频的姿势跟踪的能力，了解其复杂行为的目标将大大提高。但是，跟踪需要高质量的注释数据集的APE照片。在这里，我们介绍了开放型，这是一个新的公共数据集，该数据集由71,868张照片，带有16个身体地标的注释，这些照片在自然主义背景下有6种猿类。我们表明，与在猴子（特别是openmonkeypose数据集）和人类（可可）CAN上训练的网络相比，接受猿类照片训练的标准深网（HRNET-W48）可以可靠地跟踪样本外猿照片。这个受过训练的网络几乎可以跟踪猿类，而其他网络可以跟踪各自的分类单元，而在没有六种猿类物种之一的情况下训练的模型可以比猴子和人类模型可以更好地跟踪被固定物种。最终，我们的分析结果突出了大型专业数据对动物跟踪系统的重要性，并确认了我们新的APE数据库的实用性。
Because of their close relationship with humans, non-human apes (chimpanzees, bonobos, gorillas, orangutans, and gibbons, including siamangs) are of great scientific interest. The goal of understanding their complex behavior would be greatly advanced by the ability to perform video-based pose tracking. Tracking, however, requires high-quality annotated datasets of ape photographs. Here we present OpenApePose, a new public dataset of 71,868 photographs, annotated with 16 body landmarks, of six ape species in naturalistic contexts. We show that a standard deep net (HRNet-W48) trained on ape photos can reliably track out-of-sample ape photos better than networks trained on monkeys (specifically, the OpenMonkeyPose dataset) and on humans (COCO) can. This trained network can track apes almost as well as the other networks can track their respective taxa, and models trained without one of the six ape species can track the held out species better than the monkey and human models can. Ultimately, the results of our analyses highlight the importance of large specialized databases for animal tracking systems and confirm the utility of our new ape database.
