Automated monitoring of health and welfare in groups of pigs using evidential reasoning and video-analytics

该项目的总体目标是利用人工智能（AI）建立猪健康预警系统。为此，我们将使用AI来分析猪圈的视频流。人工智能系统将监控围栏内猪之间的社交互动以及它们的个体行为。人工智能系统将随着时间的推移建立围栏内群体行为的图片，并识别与正常模式的偏差。我们要探讨的假设是，群体行为的微妙变化可能预示着未来的健康和福利问题。我们的目标是开发一个早期预警系统，提醒农民，以便他们能够在出现健康和福利问题的第一时间迅速干预，防止问题变得更加严重。人类对群体动态中可能指示早期疾病或异常行为细微变化的观察是不切实际的，消费和现代猪系统同时处理数百或数千个个体。我们将使用AI来解决这个问题。一个摄像头将监控围栏，我们的人工智能计算机视觉软件将跟踪每头猪的运动，识别进食和饮水等活动，并识别猪之间的互动。为了将所有这些信息联合收割机结合起来，以给出群体行为的完整画面，我们将使用证据推理网络（ERN）。这些网络提供了一种原则性的方法，可以将多个来源的不确定证据结合起来，比如多只猪的活动，从而给出一个连贯的世界图景。通过结合与多只猪的行为相关的证据，以及它们之间的相互作用，我们的目标是在群体层面上理解行为模式。检测群体行为的细微变化将成为我们早期预警系统产生警报的基础。学生将进行以下活动：1）建立和扩展现有的计算机视觉工具，用于监测动物行为。目的是开发一个能够长期运行的实时跟踪和活动识别系统。该系统将长期记录每头猪的位置及其活动，如社交互动、玩耍、进食和饮水事件。目前的方法只能应用于有限的时间段，因为它们需要昂贵的离线非实时处理。2）给定一个长期猪行为信息的数据集，我们将应用ERN将跟踪器提供的联合收割机轨迹信息与基于深度学习的事件和活动检测方法（如动物姿势、进食和饮水行为以及社交互动）结合起来。我们将设计证据网络来推理和联合收割机多个低级别的证据来源，以触发警报相关的组级别的行为变化。3）检测组级别的交互/事件，同时减少对手动标记的数据集的依赖，我们将探索自动数据挖掘长期行为数据集的方法。为此，我们将开发正常猪行为的统计模型，并开发检测当前行为是否偏离正常的方法。群体行为的变化将触发警报，发出福利事件的警告。4）我们最终将联合收割机的所有组件结合在一起，开发一个完整的系统，了解猪的群体行为。该系统将能够连续监测动物福利和健康在真实的时间，以触发警报，并使早期干预，如果需要的。除了明显的健康和福利效益，该系统将有可能开辟一个新的研究领域，使数据挖掘应用于新的大型动物行为数据集的方式，以前没有可能。

The overall aim of this studentship is to use artificial intelligence (AI) to build an early warning system for pig health. To do this we will use AI to analyse a video stream of a pig pen. The AI system will monitor the social interactions between pigs within a pen as well as their individual behaviours. The AI system will build up a picture of the group behaviour within the pen over time and identify deviations from the normal pattern. The hypothesis we want to explore is that subtle changes in group behaviour can indicate future health and welfare problems. We aim to develop an early warning system to alert farmers so that they can quickly intervene at the first sign of health and welfare problems to prevent them from becoming more serious.On a commercial scale, human observation of subtle changes in group dynamics that may indicate early-stage disease or aberrant behaviour is impractical as such observations are time-consuming and modern pig systems deal with hundreds or thousands of individuals simultaneously. Instead, we will use AI to address this problem. A video camera will monitor the pen, and our AI computer-vision software will track the movements of each pig, recognise activities such as eating and drinking and recognise interactions between pigs. To combine all this information to give a complete picture of the group-level behaviour we will use Evidential Reasoning Networks (ERN). These networks provide a principled way to combine multiple sources of uncertain evidence, such as the activities of multiple individual pigs, to give a coherent picture of the world. By combining evidence related to the behaviour of multiple individual pigs, together with their interactions, we aim to achieve an understanding of the pattern of behaviours at the group level. Detecting subtle changes at the group level behaviour will form the basis for the alerts generated by our early warning system.The following activities will be performed by the student:1) Build on and extend existing computer-vison tools for monitoring animal behaviour. The aim will be to develop a real-time tracker and activity recognition system capable of long-term operation. The system will produce a long-term record of the location of each pig and its activities such as social interactions, play, and feeding and drinking events. Current approaches can only be applied for limited time periods as they require costly offline non-real-time processing.2) Given a dataset of long-term pig behaviour information, we will apply ERN to combine trajectory information, provided by the tracker, together with deep-learning-based event and activity detection methods such as animal posture, feeding and drinking behaviours and social interactions. We will design evidential networks to reason about and combine multiple sources of low-level evidence in order to trigger alerts related to group-level behavioural changes.3) To detect group-level interactions/events, while reducing reliance on manually labelled datasets, we will explore ways to automatically data-mine long-term behavioural datasets. To do this we will develop a statistical model for normal pig behaviour and develop methods to detect whether the current behaviour is deviating from normal. Changes in group behaviour will then trigger alerts giving warnings of welfare events.4) We will finally combine all our components together to develop a complete system for understanding the group behaviour of pigs. The system will be capable of continuously monitoring animal welfare and health in real time in order to trigger alerts and enable early intervention if needed. In addition to the obvious health and welfare benefits, this system will have the potential to open a new field of research, enabling data mining to be applied to newly available large datasets of animal behaviour in ways that have not been possible before.