交通运输环境下的运动车辆检测

Moving Vehicle Detection in Transportation Environment

交通运输环境下的运动车辆检测是近年来计算机视觉以及图像处理领域研究的热点。随着交通联网的普及,交通运输行业对运动目标检测的精确性以及对复杂背景环境的适应性的需求越来越高。因此围绕如何提高交通运输环境下运动目标检测和背景检测的准确度两个方面进行研究,对帧差法和背景差分法改进后融合,并将融合算法运用到复杂的交通环境中。以大量交通录像视频作为样本,用改进的融合算法进行了测试。实验结果表明该算法能够精确快速的检测出目标,完整还原出物体轮廓,有效减小噪声影响,具有良好的抗干扰性。

Moving vehicle detection in transportation environment is a research hotspot in thefield of computer vision and image processing in recent years With the popularity of transportation networking,the demand of transportation industry for the accuracy of moving target detection and the adaptability to complex background environment is higher and higher.Therefore,this paper focuses on how to improve the accuracy of moving target detection and background detection in transportation environment improves the fusion of frame difference method and background difference method,and applies the fusion algorithm to complex transportation environment.Taking a large number of traffic videos as samples,the improved fusion algorithm is tested Experimental results show that the algorithm can accurately and quickly detect the target,completely restore the object contour effectively reduce the influence of noise and has good anti-interference.