从森林到动物园:大猿与黑猩猩的行为识别行为行为
Michael Fuchs1, Emilie Genty2, Adrian Bangerter3
1Institute of Information Management, University of Neuchâtel, Neuchâtel, Switzerland.
概括
这项研究介绍了ChimpBehave,这是一个用于自动识别黑猩猩行为的新数据集,克服了手动注释的挑战. 它可以在不同的环境中测试机器学习模型,以更好地保护和研究.
科学领域:
- 灵长类动物的种族学
- 计算机视觉 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 自动行为识别对于灵长类研究和保护至关重要.
- 手动视频注释耗时,阻碍了大规模研究.
- 机器学习模型在各种数据收集上下文和注释中扎.
研究的目的:
- 介绍ChimpBehave,这是一个用于识别黑猩猩行为的新型数据集.
- 能够研究用于机器学习模型的跨数据集概括.
- 建立自动行为识别模型的性能基线.
主要方法:
- 开发的黑猩猩行为数据集: ~2小时20分钟的动物园住宿的黑猩猩视频.
- 标注有边界框和细粒度的机车行为.
- 将行为类与PanAf数据集对齐,用于跨数据集概括研究.
主要成果:
- 基于视频和基于骨架的基准的最先进的动作识别模型.
- 建立了数据集内部和跨数据集评估的绩效基准.
- 突出了不同模型架构的优点和局限性.
结论:
- ChimpBehave数据集有助于在各种环境中对自动行为识别进行研究.
- 研究结果为应用机器学习用于灵长类动物行为分析提供了洞察力.
- 数据集,模型和代码可供公众进一步研究.
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