Study on the Detection of Dairy Cows' Self-Protective Behaviors Based on Vision Analysis
Study on the Detection of Dairy Cows' Self-Protective Behaviors Based on Vision Analysis
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基于视觉分析的奶牛自我保护行为检测研究
DOI：
10.1155/2018/9106836
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Chuanzhong Xuan
中科院分区：
文献类型：
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作者：
Jia Li;Pei Wu;Feilong Kang;Lina Zhang;Chuanzhong Xuan
研究受双翅目昆虫侵染的奶牛的自我保护行为，对于评价饲养环境和奶牛的选育具有重要意义。目前测量奶牛自我保护行为的做法大多是通过人的观察，不仅繁琐，而且效率低，不准确。本文开发了一个基于视频分析的自动监控系统。首先，提出了一种改进的基于Shio-Tomasi角点检测的光流跟踪算法。该方法结合了头部、腿部和尾部运动的形态特征，有效地减少了Shio-Tomasi点的数量，消除了背景运动的干扰，降低了算法的计算复杂度，提高了检测精度。该检测算法用于通过人工神经网络计算尾部、腿部和头部的运动次数。尾部和头部的准确率达到[0.88，1]，召回率为[0.87，1]。本文提出的方法提供了客观的测量方法，可以帮助研究人员在奶牛饲养和管理过程中更有效地分析奶牛的自我保护行为和生存环境。
The study of the self-protective behaviors of dairy cows suffering dipteral insect infestation is important for evaluating the breeding environment and cows’ selective breeding. The current practices for measuring diary cows’ self-protective behaviors are mostly by human observation, which is not only tedious but also inefficient and inaccurate. In this paper, we develop an automatic monitoring system based on video analysis. First, an improved optical flow tracking algorithm based on Shi-Tomasi corner detection is presented. By combining the morphological features of head, leg, and tail movements, this method effectively reduces the number of Shi-Tomasi points, eliminates interference from background movement, reduces the computational complexity of the algorithm, and improves detection accuracy. The detection algorithm is used to calculate the number of tail, leg, and head movements by using an artificial neural network. The accuracy range of the tail and head reached [0.88, 1] and the recall rate was [0.87, 1]. The method proposed in this paper which provides objective measurements can help researchers to more effectively analyze dairy cows’ self-protective behaviors and the living environment in the process of dairy cow breeding and management.