| 智能视频分析的车辆异常行为检测方法 |
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| 引用本文： | 尹宏鹏,李艳霞,周佳怡,柴毅.智能视频分析的车辆异常行为检测方法[J].保鲜与加工,2016,16(3):75-83. |
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| 作者姓名： | 尹宏鹏 李艳霞 周佳怡 柴毅 |
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| 作者单位： | 国家山区公路工程技术研究中心, 重庆 400060;重庆大学 自动化学院, 重庆 400044,重庆大学 自动化学院, 重庆 400044,重庆大学 自动化学院, 重庆 400044,重庆大学 自动化学院, 重庆 400044 |
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| 基金项目： | 国家山区公路工程技术研究中心开放基金资助项目(GSGZJ-2014-07)。 |
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| 摘 要： | 为验证公司自主研发的呋喃它酮代谢物化学发光微粒子检测试剂盒的检测效果,用化学发光微粒子免疫法和高效液相色谱串联质谱法对猪肉、鸡肉、鱼肉、虾4个样品中呋喃它酮代谢物残留量进行检测,比对两种方法试验结果间的差异。结果表明,使用直接竞争CLIA试剂盒检测动物性食品中呋喃它酮代谢物残留量,其特异性强,灵敏度较高,在猪肉、鸡肉、鱼肉、虾样品中0.2、0.4、0.8μg/kg 3个水平的加标回收率均在94.0%~101.0%之间,变异系数均小于15%,最低检测限分别为86.24、84.09、84.51、88.12 ng/kg;此方法与高效液相色谱串联质谱法检测实际样品的阴、阳性判断结果一致,其结果稳定、可靠,可满足现场快速检测动物组织中呋喃它酮代谢物残留的要求。
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| 关 键 词： | 车辆异常行为检测 目标检测 背景差分 均值漂移 |
| 收稿时间： | 2016/1/2 0:00:00 |
A vehicle abnormal behavior detection method based on intelligent video analysis |
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| YIN Hongpeng,LI Yanxi,ZHOU Jiayi and CHAI Yi.A vehicle abnormal behavior detection method based on intelligent video analysis[J].Storage & Process,2016,16(3):75-83. |
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| Authors: | YIN Hongpeng LI Yanxi ZHOU Jiayi and CHAI Yi |
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| Institution: | Fund of National Engineering and Research Center for Mountainous Highways, Chongqing 400044, P. R. China;College of Automation, Chongqing University, Chongqing 400044, P. R. China,College of Automation, Chongqing University, Chongqing 400044, P. R. China,College of Automation, Chongqing University, Chongqing 400044, P. R. China and College of Automation, Chongqing University, Chongqing 400044, P. R. China |
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| Abstract: | In this paper, an intelligent-video-analysis-based vehicle abnormal behavior detection method was presented to handle the real-time problem in vehicle abnormal behavior detection. When vehicle abnormal behavior occurs, vehicle position, velocity and moving direction change rapidly. To extract the changes of the three parameters mentioned above, the background subtraction approach was adapted to detect vehicles. Furthermore the meanshift algorithm was utilized to track the detected vehicles. Vehicle behavior decision can be concluded by weight fusion of the three parameters. To verify the proposed method, experiments on real videos were operated. Experimental results demonstrate that the proposed method can detect vehicle abnormal behavior effectively in real traffic scene. |
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| Keywords: | vehicle abnormal behavior detection object detection background difference mean shift |
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