The increasing demand for sustainable livestock products also demands new considerations in animal breeding. Breeding programs are now seeking to integrate animal behavioural phenotypes, as these relate to the productivity, health and welfare of the animals and thereby can influence yield and economic benefits in the industry. Traditional manual observation of pig behaviour is time-consuming, laborious, subjective, and difficult to achieve in continuous and large-scale operations. It is not surprising that computer vision technology with the advantages of being objective, non-invasive and continuous has been widely researched for its use in the recognition of livestock behaviours over recent years. Nevertheless, in studies of livestock behaviour recognition, computer vision technology faces some challenges, e.g., complex scenes, variable illumination, occlusion, touching and overlapping between livestock, which has limited the fast translation of technology to industry. On the other hand, deep learning technology has proven to solve these difficulties to a certain extent and is being adopted to recognise livestock behaviours. This paper mainly evaluates the recent developments in computer vision methods for recognition of these behaviours in pigs and cattle. The focus on these species is made possible by the number of studies exist quantifying behaviours that are of importance for their health, welfare and productivity such as aggression, drinking, feeding, lameness, mounting, posture, tail-biting and nursing. This review paper especially analyses the development of image segmentation, identification and behaviour recognition using tradition computer vision and more recent deep learning methods, and evaluates the evolution of key research in the field. We elaborate the research trend of livestock behaviour recognition from four aspects, i.e., development of robust livestock identification algorithms, recognition of livestock behaviours for different growth stages, further quantification of the results of behaviour recognition, and building evaluation system of growth status, health and welfare.

对可持续畜产品的日益增长的需求也要求在动物育种方面进行新的考虑。育种计划现在正在寻求整合动物行为表型，因为这些行为表型与动物的生产力、健康和福利有关，从而可以影响该行业的产量和经济效益。传统的人工观察猪的行为耗时、费力、主观，在连续、大规模的操作中难以实现。近年来，计算机视觉技术以其客观、非侵入性和连续性等优点在牲畜行为识别中的应用得到广泛研究，这并不奇怪。然而，在牲畜行为识别的研究中，计算机视觉技术面临着一些挑战，例如复杂的场景、牲畜之间的可变光照、遮挡、接触和重叠，限制了技术向工业的快速转化。另一方面，深度学习技术已被证明在一定程度上解决了这些困难，并被用于识别牲畜行为。本文主要评估了用于识别猪和牛这些行为的计算机视觉方法的最新进展。对这些物种的关注是由于存在量化对其健康、福利和生产力很重要的行为的数量的研究，例如攻击性、饮酒、喂食、跛足、爬行、姿势、咬尾和护理。这篇综述论文特别分析了图像分割的发展，使用传统计算机视觉和最近的深度学习方法进行身份识别和行为识别，并评估该领域关键研究的演变。我们从四个方面阐述了家畜行为识别的研究趋势，即鲁棒性家畜识别算法的发展、家畜不同生长阶段行为的识别、行为识别结果的进一步量化，以及建立生长状态、健康和健康评价体系。福利。