基于背景模型的车辆目标检测

为了实现对交通车辆快速准确地统计,文中提出一种自适应权值的背景更新方法以适应道路环境的复杂变化.首先在多个通道建立单高斯背景统计模型,然后利用场景中像素的概率分布实现对运动区域的准确检测,最后根据检测结果实现对交通流量的统计.实验结果表明:该方法能够对运动车辆进行快速准确地检测和统计,并对场景的光照变化等影响具有较高的鲁棒性.     

基于车道信息融合的车辆行为识别

在车辆的自动驾驶和辅助驾驶中,实时分析车辆的运动状态具有重要的实际应用价值。为了实现对车辆行为的判断,提出一种基于车道信息融合的车辆行为识别算法。首先提出一种基于改进Robinson与LSD的模型,运用改进的Robinson算子获取最佳梯度幅值实现对车道的边缘提取,再通过LSD算法实现车道的检测。然后采用一种基于滑动窗口的三次样条插值法对车道进行拟合,最后根据车道参数信息分析车辆的运动状态,结合车辆的中心位置得到车辆的偏离信息。在BDD100K数据集的测试中,本文算法的车道检测准确率为95.61%,车辆行为识别准确率为93.04%,每秒传输帧数达到42.37。实验结果表明,本文算法在不同场景下可以有效地区分车辆的运动状态并给出车辆的偏离信息,具有更高的准确性和鲁棒性。     

基于滑动窗的混合高斯模型运动目标检测方法

在复杂场景下,传统混合高斯模型能较好地检测出运动目标,但随着时间的推移,模型参数收敛缓慢且难以适应场景中真实背景的实时变化,从而导致运动目标的错误检测率增加。该文利用滑动窗技术的短时历史记忆特性,提出一种新颖的基于滑动窗的混合高斯模型运动目标检测方法,该方法弥补了传统混合高斯背景模型不能及时形成新背景的缺点,提高了运动检测的完整性,并进一步降低了算法对场景光照变化的敏感性。多场景下的对比实验结果表明,该方法能更准确、完整地检测出运动目标并具有更好的环境适应性。     

基于帧差法的运动车辆阴影去除算法

在视频交通车辆目标检测中,阴影问题是影响其检测准确性的关键问题之一.为了解决这个问题,提出了一种结合单模高斯模型和帧差法的运动目标阴影去除方法.首先通过单模高斯模型背景建模获取前景包括阴影在内的目标图像,再结合帧差法去除阴影.实验结果证明,该方法得到的车辆目标比较完整,并较好地去除了运动车辆阴影.     

基于车牌识别的机混车道视频测速算法

通过对视频测速应用场景的分析,提出一种在机混车道中应用视频测速的方法。该方法首先采用二维图像标定的方法获得视频坐标与实际地面坐标之间的映射关系,针对机混车道的复杂场景,采用Lab颜色空间和形态学处理相结合的方法对车牌进行识别,准确地跟踪到目标车辆,有效地避免了其他运动物体的干扰,同时提出了采用模板更新的方式防止目标车辆跟丢。最后通过适当选取车辆经过检测区时的图像帧,获得车辆的位移及对应的时间间隔,从而获得高精度的速度测量结果。     

基于深度学习的车道线激光精准检测方法研究

车道线检测是智能辅助驾驶的核心问题，研究基于深度学习的车道线激光精准检测方法，提高车道线检测精度。将激光雷达安装在待检车辆上，用来扫描物体返回脉冲，通过激光雷达的回波脉冲宽度提取车道线与路面区分度的特征，结合深度学习方法，构建新型多尺度全卷积神经网络的车道线检测模型，融合特征图对车道线分割，实现车道线精准检测。实验结果表明，在多场景条件下，本方法的漏检率、误检率均低于传统方法，平均检测合格率96%,交互比也远远高于传统方法，平均耗时低至51 ms,且变化态势平稳，说明本方法在不同场景中车道线图像分割处理效果较好，车道线的检测效率较高，车道线检测的精度较高。     

基于改进混合高斯背景模型的车辆检测算法研究

传统的混合高斯背景模型对光照突变十分敏感,对运动车辆检测效果不理想。为此,本文提出了一种基于改进混合高斯背景模型的车辆检测算法,利用不匹配像素来消除光照突变对背景建模产生的影响。实验结果表明,与传统的混合高斯背景模型算法相比,在光照突变明显的条件下,改进后的算法更能有效检测出运动车辆。     

改进高斯混合模型的运动目标检测算法

传统的高斯模型无法检测比较复杂的场景或速度较低的运动目标,因此提出基于改进高斯混合模型的运动目标检测算法。使用多个高斯模型表示运动目标图像内各像素点特征,并基于图像内各像素点与高斯混合模型相匹配则视其为背景点,反之为前景点原理,更新高斯混合模型。通过更新前景模型并计算短时稳定度指标,提高运动目标检测效果,通过确定高斯分布与像素关系,设定新的参数构造背景模型,消除光照突变造成的影响。实验分析结果表明,该方法能够很好地检测与跟踪运动目标,且抗噪性能好、清晰度高,准确率高达98%。     

基于车底阴影的车前障碍物检测

基于计算机视觉的道路车辆检测是智能车辆导航的核心问题,实时准确地检测前方运动车辆的位置信息是车辆安全驾驶的前提。文中采用车底阴影的前方运动车辆检测算法,在基于车道线检测的基础上,通过车底阴影检测,实时准确地检测前方车辆。该算法通过使用Otsu阈值分割提取出车道线,生成AOI区域,再进行两次自适应阈值分割提取车底与路面的交线,从而检测出前方运动车辆。经过在高速公路上对运动车辆检测实验证明,该算法基本满足车辆安全驾驶的需求,并能准确实时地检测出前方运动车辆,进而减少了交通事故的发生。     

基于高斯模型的运动目标检测方法研究

提出了一种改进的基于单高斯背景模型运动目标的检测算法。该算法用四帧差分法确定运动目标区域,用单高斯模型对背景进行更新,然后通过(r,g,I)特征空间去除阴影,从而提取运动目标。实验结果表明,该算法能在变化的场景中对背景进行实时更新,能快速检测出运动目标,算法的运算量小。在室内环境和背景较为稳定的室外环境中都具有较好的应用价值。     

基于PEV的双向DC/DC变换器的研究

介绍了一种纯电动汽车超级电容器充放电系统的大功率双向DC/DC变换器。首先给出了纯电动汽车电传动系统的结构图,然后介绍了双向DC/DC变换器的拓扑,并针对超级电容器充放电系统设计双向DC/DC变换器的控制器。     

Image-Based Traffic Monitoring With Shadow Suppression

For a vision-based traffic monitoring and enforcement system, shadows of moving objects often cause serious errors in image analysis due to misclassification of shadows and moving vehicles. An effective shadow suppression method is thus required to improve the accuracy of image analysis and this paper proposes a novel color-space ratio model for detecting shadow pixels in traffic imagery. The proposed approach does not require many image sequences for constructing the model. Instead the model can be easily built up using a shadow region in a single image frame. To increase the accuracy of shadow detection, we design two types of spatial analysis to verify actual shadow pixels. Comparative results show that the proposed method works better than several well-known methods. The proposed methods have been applied to an image-based traffic monitoring system for detecting shadow pixels in traffic imagery. The experimental results not only validate the feasibility of the proposed algorithm but also successfully estimate traffic parameters such as traffic flows, traffic densities, vehicle turn ratios and vehicle speeds, all with satisfactory accuracy     

Traffic flow detection and statistics via improved optical flow and connected region analysis

Moving vehicle detection plays an important role in intelligent transportation systems. One of the common methods used in moving vehicle detection is optical flow. However, conventional Horn–Schunck optical flow consumes too much time when calculating dense optical flows so that it cannot meet the real-time requirements. This paper proposes a novel improved Horn–Schunck optical flow algorithm based on inter-frame differential method. In our algorithm, optical flow field distribution is only calculated for pixels with larger gray values in the difference image, while for other pixels we applied the iterative smooth. The number of vehicles in the videos of traffic conditions is counted by setting the virtual loop and detecting optical flow information. To extract the moving vehicle as accurately as possible, we also propose a method to obtain moving vehicle minimum bounding rectangle based on the connected region analysis. Finally, we compare the improved optical flow with other four optical flow algorithms in moving vehicle extraction and vehicle flow detection, from which our method gives a much more accurate result.     

Robust shape-matching criterion for precision tracking of moving objects in correlated image sequences

A robust shape-matching measure with a boundary detection preprocessor is proposed to track a moving object in complex image sequences. A correlation-tracking algorithm seeks to align the incoming target image with the reference image of the target. But it has a critical problem if we use the conventional block-matching criteria. This is the so-called 'false peak problem', which is generally caused by highly correlated background pixels with similar intensities to the pixels of moving targets. The robustness of the proposed method for practical application is demonstrated by simulating two kinds of real-target image sequences.     

视频多运动目标提取的新方法

提出一种利用视频序列的时域和空域信息进行多运动目标检测的新方法.首先对相邻的图像帧进行差分,并利用3σ准则二值化差分图像,得到运动目标的时域信息,然后标记该二值图像的连通区域来消除噪声,结合边缘检测所得到的空域信息得到运动目标模型,最后连接模型的最外围轮廓点即得到目标的完整信息.该算法充分运用了运动目标的时域、空域两方面信息,能够准确的提取出运动目标.     

一种基于Phong物体光照模型的阴影检测算法

针对目前运动目标检测算法中常将阴影误检为前景目标的问题,提出一种基于Phong物体光照模型的阴影检测算法。基于Phong物体光照模型,我们对场景中象素的亮度值进行分析,通过定义一个亮度相对变化量,推导出他在整个阴影区域是比较稳定的,所以在一个(5×5)的模板上用协方差来衡量这种稳定性,从而得到第一个阴影判决式。又推导出阴影区域亮度相对变化量随时间的变化保持相对稳定,设计一个滤波模板来增大目标区域的不稳定性,从而得到第二个阴影判决式。最后结合以上二个阴影判决式进行阴影检测,并对实验结果进行定性和定量的评估。与前人提出算法比较,本文提出的算法在阴影检测率和区分率等方面都得到了提高,具有较强的鲁棒性。     

基于多视InSAR相干性估计误差模型的相干性迭代估计

文章全面分析了引起InSAR多视相干性估计误差的因素,建立了与区域增长相干性估计方法对应的多视InSAR相干性估计误差模型,提出了一种新的基于该误差模型的相干性迭代估计方法.该方法首先利用基于强度图像的区域长原理计算相干性,区域增长保证窗里的所有像素属于同一分布,从而能够消除不同分布样本导致的相干性估计误差;后利用所建立的多视相干性与真实相干性的非线性模型对所得的相干性估计值进行高斯牛顿迭代,迭代可以减少由于相性太低和估计样本太少导致的相干性估计误差,得到更加准确的相干性估计.     

A fast image super-resolution algorithm using an adaptive Wiener filter.

A computationally simple super-resolution algorithm using a type of adaptive Wiener filter is proposed. The algorithm produces an improved resolution image from a sequence of low-resolution (LR) video frames with overlapping field of view. The algorithm uses subpixel registration to position each LR pixel value on a common spatial grid that is referenced to the average position of the input frames. The positions of the LR pixels are not quantized to a finite grid as with some previous techniques. The output high-resolution (HR) pixels are obtained using a weighted sum of LR pixels in a local moving window. Using a statistical model, the weights for each HR pixel are designed to minimize the mean squared error and they depend on the relative positions of the surrounding LR pixels. Thus, these weights adapt spatially and temporally to changing distributions of LR pixels due to varying motion. Both a global and spatially varying statistical model are considered here. Since the weights adapt with distribution of LR pixels, it is quite robust and will not become unstable when an unfavorable distribution of LR pixels is observed. For translational motion, the algorithm has a low computational complexity and may be readily suitable for real-time and/or near real-time processing applications. With other motion models, the computational complexity goes up significantly. However, regardless of the motion model, the algorithm lends itself to parallel implementation. The efficacy of the proposed algorithm is demonstrated here in a number of experimental results using simulated and real video sequences. A computational analysis is also presented.     

基于多帧边缘差异的视频运动对象的分割与跟踪算法

从视频场景中分割和跟踪感兴趣的视频对象对于MPEG-4等基于对象的视频编码来说是关键性的技术之一。针对目前大部分视频对象分割和追踪算法相当复杂但仍不能有效地去除背景噪声的问题,该文提出用于分割和跟踪视频运动对象的一种基于多帧边缘差异的算法。该算法利用一组帧的边缘差异来提取运动对象区域,通过聚类方法去除背景像素点,利用形态学算子得到对象分割模板,同时通过建立前帧感兴趣对象与当前帧运动对象的帧间向量跟踪当前帧的感兴趣视频对象。不同标准视频测试序列的测试结果表明,该算法能够实现对感兴趣的视频运动对象更为精确、快速和有效地分割和跟踪。