摘要
过去采用数据融合方法进行的汽车目标跟踪,未对目标图像信息有效处理,图像噪声影响较强,无法准确的对目标实施识别和跟踪,研究红外图像目标跟踪在智能网联汽车的应用过程,采用红外图像成像原理获取智能网联汽车的目标红外图像,通过滤波降噪、图像增强和图像分割操作降低红外图像的噪声、增强目标与背景对比度,实现目标与红外图像背景的有效分割;在此基础上,采用均值漂移目标跟踪算法,在新的红外图像中确定目标待选位置,并确保描述目标直方图与备选目标直方图概率分布相似度的巴氏系数最大,实现智能网联汽车对目标的准确跟踪。实验结果说明,红外图像目标跟踪在提升智能网联汽车目标识别结果上有显著作用,识别简单场景和复杂场景目标的正确率分别为0. 966和0. 565,都高于数据融合方法,且目标跟踪效率高。
In the past,the method of data fusion was used to track car targets. It did not effectively process target image information,and the image noise had a strong influence. It was impossible to accurately identify and track targets,and the application process of infrared image target tracking in intelligent network vehicles was studied. The infrared image imaging principle is used to obtain the infrared image of the intelligent network car. The noise reduction,image enhancement and image segmentation operation of the filter wave reduce the noise of the infrared image,enhance the contrast between the target and the background,and achieve the effective segmentation of the target and the infrared image background. On this basis,using the mean drift target tracking algorithm,the target’s location is determined in the new infrared image,and the Barthel coefficient describing the probability distribution similarity between the target histogram and the alternative target histogram is ensured to realize the accurate tracking of the targets of the intelligent network car. The experimental results show that infrared image target tracking plays a significant role in improving the target recognition results of intelligent network-connected vehicles. The accuracy rates of simple scene recognition and complex scene target reach up to 0.966 and 0.565,respectively,which are higher than the data fusion method and the target tracking efficiency.
作者
兰轶
杨澜
LAN Yi;YANG Lan(Information and Network Management Office of Chang 'an University, Xi 'an 710064, China;School of Information Engineering, Chang 'an University,Xi 'an 710064 , China)
出处
《激光杂志》
北大核心
2019年第7期60-64,共5页
Laser Journal
基金
国家自然科学基金项目(No.61703053)
关键词
红外图像
目标跟踪
智能
网联汽车
分割
均值漂移
巴氏系数
infrared images
target tracking
intelligence
networkvehicles
segmentation
mean drift
Barthel coefficient
