Recognizing piglet play, in all its forms, using machine learning on data from accelerometers and computer vision

利用加速度计和计算机视觉数据的机器学习来识别各种形式的小猪玩耍

• 批准号: 577142-2022
• 负责人: AhloyDallaire, JamieJ
• 金额: $ 3.24万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765069
• 关键词: Recognizing; piglet; play; its; forms

Play in young farm animals is of interest to animal behaviour scientists concerned with how behaviour evolves and develops, and can be used to assess and improve animal welfare. However, research and practical applications require that this behaviour be measured efficiently and automatically. Accelerometers are ideally suited to detect play, which is characterized by forceful and sudden movements. Computer vision is another promising tool that enables tracking of animals in video, and can recognize certain behaviours. We aim to develop and compare accelerometer and video-based tools, trained using machine learning to automatically detect different types of play in piglets, classifying them in the same way as human observers.Our first objective is to design custom accelerometers to be worn on piglet legs and ears. Our second objective is to train and validate machine learning algorithms to detect play. We will collect video and accelerometer data from pairs of piglets in a play arena. Computer vision algorithms will determine animal positions and movements. Data will be synchronized to behavioural categorizations made by human observers, and machine learning models trained to classify types of play (locomotor, object, social) and other behaviours (e.g. aggression) using data from ear accelerometers, leg accelerometers, or animal tracking by computer vision. Our third objective is to test how these methods perform in typical farm conditions, in Québec and Manitoba. They should correctly identify differences between individuals and between groups of piglets, and perform as well as human observers, categorizing behaviour the same way in a fraction of the time.This research will have valuable scientific and societal impacts. Automated tools will drastically expand our capacity to study the nature of play and its significance for animal welfare. Tools developed will be openly disseminated, readily adaptable by other researchers. We will encourage practical applications by presenting results directly to the pork industry. The project will enable research on a fascinating type of animal behaviour, and applications to improve the lives of farm animals in increasingly sustainable food production systems.

年幼农场动物的玩耍对动物行为科学家感兴趣，他们关注行为如何进化和发展，并可用于评估和改善动物福利。然而，研究和实际应用需要有效和自动地测量这种行为。加速度计非常适合检测剧烈而突然的运动。计算机视觉是另一种有前途的工具，可以在视频中跟踪动物，并识别某些行为。我们的目标是开发和比较加速度计和基于视频的工具，使用机器学习进行训练，以自动检测小猪的不同玩耍类型，并以与人类观察者相同的方式对其进行分类。我们的第一个目标是设计定制的加速度计，用于佩戴在小猪的腿和耳朵上。我们的第二个目标是训练和验证机器学习算法来检测游戏。我们将收集视频和加速度计数据从对小猪在一个发挥竞技场。计算机视觉算法将确定动物的位置和运动。数据将与人类观察者进行的行为分类同步，并训练机器学习模型，使用来自耳朵加速度计，腿部加速度计或计算机视觉动物跟踪的数据对游戏类型（运动，物体，社交）和其他行为（例如攻击）进行分类。我们的第三个目标是测试这些方法在魁北克和马尼托巴典型农场条件下的表现。他们应该正确识别个体之间和仔猪群体之间的差异，并表现得与人类观察者一样好，在很短的时间内以相同的方式对行为进行分类。这项研究将产生宝贵的科学和社会影响。自动化工具将极大地扩展我们研究玩耍本质及其对动物福利意义的能力。所开发的工具将公开传播，其他研究人员可随时加以修改。我们将通过直接向猪肉行业展示结果来鼓励实际应用。该项目将使人们能够研究一种迷人的动物行为，并应用于改善日益可持续的食品生产系统中农场动物的生活。