基于混合高斯模型的移动奶牛目标实时检测  

Real-time Target Detection for Moving Cows Based on Gaussian Mixture Model

作　　者：刘冬[1] 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,陕西杨凌712100

出　　处：《农业机械学报》2016年第5期288-294,共7页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家自然科学基金项目(61473235)

摘　　要：针对奶牛养殖场背景复杂和环境多变导致现有的目标检测算法无法满足鲁棒性和实时性需求的问题,基于递归背景建模思想,在混合高斯模型中引入惩罚因子,提出了一种动态背景建模方法,采用局部更新策略,以降低模型复杂度和解决前景消融问题;提出基于色度偏差和亮度偏差的二分类算法,避免目标物阴影区域的影响。对不同天气及环境变化剧烈情况下获取的奶牛视频样本进行实验。结果表明,与混合高斯模型相比,平均模型复杂度降低了50.85%,前景误检率和背景误检率分别降低了19.50和13.37个百分点,单帧运行时间降低了29.25%,检测准确率更高、实时性更好,且解决了前景消融问题,能满足在复杂背景和环境条件下实时提取奶牛目标的要求。Target detection is the basic work for analyzing the behavior of the cows using video analysis technology. It is difficult to extract the moving cows accurately and real-timely with the existing target detection methods because of the complex background environment. In this study, a series of improvement measures were proposed based on Gaussian mixture model to meet the system requirements.A dynamic background modeling method with penalty factor was proposed for the mathematical model of the background which can overcome the high model complexity. A two-class classification algorithm based on chromaticity distortion and brightness distortion was proposed to avoid the influence of the shaded area in the foreground detection algorithm. Local update method was proposed to avoid missing the target if it stays for a long time. In order to verify the validity of the algorithm,four evaluation parameters were introduced to test the detection algorithm including model complexity,false detection rate of foreground,false detection rate of background and processing time. Experimental results show that model complexity was 50. 85% lower than the classical method. False detection rate of foreground and false detection rate of background were 18. 18% and 7. 52%,which had 19. 50 and 13. 37 percent lower than the classical Gaussian mixture model. Processing time of average single frame was 29. 25% lower. Statistics indicate that the algorithm proposed in this study can improve the detection performance and it is an extension to classical Gaussian mixture model.

关 键 词：移动奶牛 视频分析 目标检测 阴影检测 混合高斯模型 

分 类 号：S24[农业科学—农业电气化与自动化] TP319[农业科学—农业工程]