Interpretation of animal behaviours using novel video processing techniques in Artificial Intelligence

使用人工智能中的新型视频处理技术解释动物行为

• 批准号: 2750918
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2750918
• 关键词: Interpretation; animal; behaviours; using; novel

Although some progress has been made in the interpretation of human behaviours using machine learning, research into the interpretation of livestock behaviours using video footage has been more limited. This is unfortunate, as automated systems for herd monitoring could produce tangible improvements in animal welfare. What progress has been made in the development of precision livestock farming has, typically, focused on the use of physical tags or electronic devices such as accelerometers to measure and record data. Although informative, these techniques can be physically invasive or costly. There is, therefore, a need for accurate systems which can characterise and record animal behaviour using already existing cameras, as this would allow wider adoption. The goal of this project is, therefore, to develop novel techniques in machine learning and machine vision systems which will allow for automatic interpretation and recording of livestock behaviours.Data for this project will be provided by an industry partner in the form of raw or annotated video footage of dairy cows. This footage will be used to train novel machine learning systems to characterise and record important behaviours such as standing, walking, eating, drinking etc., and possibly monitor for longer term behavioural changes. Data about these behaviours could be used as an indicator of both overall herd health and the health of individual animals. Experimentation with machine learning architectures, data augmentation techniques, and data pre-processing will be necessary to produce viable solutions.

尽管在使用机器学习解释人类行为方面取得了一些进展，但使用视频片段解释牲畜行为的研究更加有限。这是不幸的，因为畜群监测的自动化系统可以切实改善动物福利。在发展精准畜牧业方面取得的进展通常集中在使用物理标签或电子设备（如加速度计）来测量和记录数据。虽然信息丰富，但这些技术可能具有物理侵入性或成本高昂。因此，需要一种精确的系统，可以使用现有的相机来描述和记录动物的行为，因为这将允许更广泛的采用。因此，该项目的目标是开发机器学习和机器视觉系统的新技术，这些技术将允许对牲畜行为进行自动解释和记录。该项目的数据将由行业合作伙伴以奶牛的原始或注释视频片段的形式提供。这些视频将用于训练新的机器学习系统，以描述和记录重要的行为，如站立、行走、进食、饮水等，并可能监测长期的行为变化。有关这些行为的数据可用作兽群总体健康和个体动物健康的指标。机器学习架构、数据增强技术和数据预处理的实验对于产生可行的解决方案是必要的。