Recognition of individual components and keypoint detection supported by instance segmentation is crucial to analyze the behavior of agents on the scene. Such systems could be used for surveillance, self-driving cars, and also for medical research, where behavior analysis of laboratory animals is used to confirm the aftereffects of a given medicine. A method capable of solving the aforementioned tasks usually requires a large amount of high-quality hand-annotated data, which takes time and money to produce. In this paper, we propose a method that alleviates the need for manual labeling of laboratory rats. To do so, first, we generate initial annotations with a computer vision-based approach, then through extensive augmentation, we train a deep neural network on the generated data. The final system is capable of instance segmentation, keypoint detection, and body part segmentation even when the objects are heavily occluded.

翻译：基于实例分割的个体组件识别与关键点检测对于分析场景中的行为主体至关重要。此类系统可应用于监控、自动驾驶，以及医学研究——其中实验室动物的行为分析常被用于验证特定药物的后遗症效应。解决上述任务的传统方法通常需要大量高质量的人工标注数据，而这类数据的获取既耗时又耗资。本文提出一种无需人工标注实验室大鼠的方法：首先通过计算机视觉方法生成初始标注，随后利用大规模数据增强对深度神经网络进行训练。最终系统即使在被观察对象存在严重遮挡的情况下，仍能实现实例分割、关键点检测与身体部位分割。