基于卷积神经网络的奶牛个体身份识别方法  

Recognition of individual dairy cattle based on convolutional neural networks

作　　者：赵凯旋[1] 何东健[1] 

机构地区：[1]西北农林科技大学机械与电子工程学院,杨凌712100

出　　处：《农业工程学报》2015年第5期181-187,共7页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金面上项目(61473235)

摘　　要：视频分析技术已越来越多地应用于检测奶牛行为以给出养殖管理决策,基于图像处理的奶牛个体身份识别方法能够进一步提高奶牛行为视频分析的自动化程度。为实现基于图像处理的无接触、高精确度、适用性强的奶牛养殖场环境下的奶牛个体有效识别,提出用视频分析方法提取奶牛躯干图像,用卷积神经网络准确识别奶牛个体的方法。该方法采集奶牛直线行走时的侧视视频,用帧间差值法计算奶牛粗略轮廓,并对其二值图像进行分段跨度分析,定位奶牛躯干区域,通过二值图像比对跟踪奶牛躯干目标,得到每帧图像中奶牛躯干区域图像。通过理论分析和试验验证,确定了卷积神经网络的结构和参数,并将躯干图像灰度化后经插值运算和归一化变换为48×48大小的矩阵,作为网络的输入进行个体识别。对30头奶牛共采集360段视频,随机选取训练数据60 000帧和测试数据21 730帧。结果表明,在训练次数为10次时,代价函数收敛至0.0060,视频段样本的识别率为93.33%,单帧图像样本的识别率为90.55%。该方法可实现养殖场中奶牛个体无接触精确识别,具有适用性强、成本低的特点。Video analysis has been widely used to perceive the behavior of animals for precise dairy farming, which is useful to increase the productivity and reduce the disease rate. Using computer vision technique to recognize the individual cow is feasible to improve the efficiency of the automatic milking and feeding system. Effective and accurate recognition of individual dairy cattle is the prerequisite and foundation to record and analyze the animal behavior automatically. As the classic method of individual recognition, the typical electronic identification device, referred to a radio frequency identification device （RFID）, must be installed on the neck or another position of the animal. But the available distance is limited and the RFID tags suffer from some shortages such as the loss of tags, tempering, and animal welfare. Besides, it requires extra device and redundant process to recognize the individual cow in a video using RFID method. Therefore, it is necessary to develop an accurate and efficient system for recognizing individual cows in feeding environment utilizing image processing method. In this paper, individual dairy cattle were recognized using the body images based on convolutional neural networks with video analysis method. Side-view images with a resolution of 704 pixels ＆#215;576 pixels were recorded when cows passed a narrow aisle to water trough. For target detecting, the frame difference method was implemented to obtain the outline and motion boundary of the cow. By dividing the target image into several same-width sections, the head and tail were removed from the image after checking the distribution of the target in the section. Because the ratio of the body’s depth to cow’s height was fixed at 0.6, the body area was located by drawing a box tangent to the back posture and then zoomed out 0.8 times of it to remove the external redundancy. For tracking the body image, template matching method was used to find the body area in the current frame by calculating the similarity against the

关 键 词：图像技术 算法 识别 卷积神经网络 深度学习 视频分析 奶牛 目标检测 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] S823.91[自动化与计算机技术—计算机科学与技术]