Automated equine behaviour monitoring

精明的看护人和兽医在很大程度上依赖于对不寻常行为的评估或对 作为身体疼痛或疾病的第一个迹象的行为。刻板印象是重复的，高度程式化的，看起来 马可能产生的无功能的运动反应和序列，并可能标志着异常行为， 行为不端或应对机制。专家(如看门人或兽医)会将刻板印象识别为 管理条件(即住房、社会、运动、营养)和遗传因素后可能出现的疾病症状 倾向性已经被考虑过了。 运动马容易患上几种潜在的严重疾病，从腿部受伤到绞痛，都可能导致突然 如果在症状出现后不久不进行治疗，就会死亡。 目前，亲身或通过网络摄像头观看运动马是唯一的行为监控解决方案 可用。 这在遵守持续监测的情况下是有效的，但这是不可持续和不寻常的行为 很多时候都没有被发现。自动监测技术将有助于克服这些限制。 该项目将研究使用计算机视觉技术自动标记异常的过程 记录马的行为，并将视频序列发送给马主人、兽医或饲养员。例如，绞痛 马的腹痛与腹痛有关，当马咬他的两侧时就会表现出腹痛， 躺下的时间比正常时间长，不断地将重心转移到后肢、爪子或有描述的 食欲或根本不吃东西。剧烈的腹痛包括大汗淋漓、持续翻滚、 坚持不懈的运动和猛烈的上下。将探索的计算机视觉技术 包括特征跟踪、光流以及骨骼动作识别技术。预期的结果 这个项目的目的是评估什么行为可以用什么技术和什么准确度来检测。

The astute caretaker and the veterinarian heavily rely on the assessing unusual behaviour or a change in behaviour as the first sign of physical pain or disease. Stereotypies are repetitive, highly stylized, seemingly functionless motor responses and sequences that horses can develop and can signifiy abnormal behaviour, misbehaviour or coping mechanism. The experts (such as the caretaker or vet) will identify stereotypies as possible disease symptoms after management conditions (i.e., housing, social, exercise, nutrition) and genetic predisposition are already considered. Sport horses are prone to several potentially serious ailments from leg injuries to colic that can lead to sudden death if not treated shortly after symptoms occur. Currently, watching sport horses in person, or by web cam, are the only behaviour monitoring solutions available. This is effective when constant monitoring is complied to but this is not sustainable and unusual behaviours many times go undetected. An automatic monitoring technique would help in overcoming these limitations. This project will investigate using computer vision techniques to automate the process of flagging unusual behaviour in the horse and sending the video sequence to the horse owner, vet or caretaker. For example, colic in horses is associated with abdominal pain and abdominal pain is exhibited when the horse bites at his sides, lays down for longer than normal, continually shifts his weight on the hind limbs, paws or has a described appetite or does not eat at all. Severe abdominal pain includes signs of profuse sweating, continuous rolling, persistent movement and getting up and down violently. Computer vision techniques that will be explored include feature tracking, optical flow as well as skeletal action recognition techniques. The anticipated result of this project is an assessment of what behaviours can be detected with what techniques and at what accuracy.