构建小鼠解机械锁盒的视频数据集，助力行为自动识别研究
Mouse Lockbox Dataset: Behavior Recognition for Mice Solving Lockboxes
- 基于关键点追踪构建动作分类框架，捕捉精细操作行为
- 包含110+小时多视角视频，提供13%人工标注标签作为基准
- 适合计算神经科学与动物行为自动化分析的研究者使用
机器学习与计算机视觉方法对自然动物行为研究影响深远，可实现海量视频数据的（半）自动分析。小鼠是多数研究领域的标准哺乳动物模型，但现有数据集多聚焦简单或社交行为。本文提出一个个体小鼠解决复杂机械谜题（称作锁盒）的视频数据集，总时长超110小时，从三个不同视角记录其行为。为帧级动作分类方法提供基准，我们对两只小鼠的视频进行人工标注，覆盖数据集总量的13%。基于关键点（姿态）追踪的动作分类框架揭示了细粒度行为自动标注的挑战，如物体操控。希望本工作能推动计算神经科学领域中自动化动作与行为分类的发展。数据集已公开，可通过 https://doi.org/10.14279/depositonce-23850 获取。
原文摘要 · Abstract (English)
Machine learning and computer vision methods have a major impact on the study of natural animal behavior, as they enable the (semi-)automatic analysis of vast amounts of video data. Mice are the standard mammalian model system in most research fields, but the datasets available today to refine such methods focus either on simple or social behaviors. In this work, we present a video dataset of individual mice solving complex mechanical puzzles, so-called lockboxes. The more than 110 hours of total playtime show their behavior recorded from three different perspectives. As a benchmark for frame-level action classification methods, we provide human-annotated labels for all videos of two different mice, that equal 13% of our dataset. Our keypoint (pose) tracking-based action classification framework illustrates the challenges of automated labeling of fine-grained behaviors, such as the manipulation of objects. We hope that our work will help accelerate the advancement of automated action and behavior classification in the computational neuroscience community. Our dataset is publicly available at https://doi.org/10.14279/depositonce-23850
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