Moving vehicles detection based on adaptive motion histogram

As one of the important topics in computer vision, moving vehicle segmentation has attracted considerable attention of researchers. However, robust detection is hampered by the interferential moving objects in dynamic scenes. In this paper, we address the problem of the moving vehicles segmentation in the dynamic scenes. Based on the distinct motion property of the dynamic background and that of the moving vehicles, we present an adaptive motion histogram for moving vehicles segmentation. The presented algorithm consists of two procedures: adaptive background update and motion histogram-based vehicles segmentation. In the adaptive background update procedure, we make use of the lighting change of the scene and present a novel method for background evolving. In the motion histogram-based vehicles segmentation procedure, an adaptive motion histogram is maintained and updated according to the motion information in the scenes, and the moving vehicles are then detected according to the motion histogram maintained. Experimental results of typical scenes demonstrate robustness of the proposed method. Quantitative evaluation and comparison with the existing methods show that the proposed method provides much improved results.     

交通场景中运动分割问题的研究

提出了一种在交通场景中鲁棒地检测运动车辆的方法，该方法采用改进的加权平均法进行实时背景学习，并根据统计量动态地计算出运动分割的阈值，以得到交通场景中的运动物体。与以往的方法相比，该方法在检测的成功率以及运算速度上均具有明显的优势。     

一种动态场景下运动对象分割新算法

视频运动对象分割是计算机视觉和视频处理的基本问题。在摄像机存在全局运动的动态场景下,准确分割运动对象依然是难点和热点问题。本文提出一种基于全局运动补偿和核密度检测的动态场景下视频运动对象分割算法。首先,提出匹配加权的全局运动估计补偿算法,消除动态场景下背景运动对运动对象分割的影响;其次,采用非参数核密度估计方法分别估计各像素属于前景与背景的概率密度,通过比较属于前景和属于背景的概率及形态学处理得到运动对象分割结果。实验结果证明,该方法实现简单,有效地提高了动态场景下运动对象分割的准确性。     

A dynamic conditional random field model for foreground and shadow segmentation

This paper proposes a dynamic conditional random field (DCRF) model for foreground object and moving shadow segmentation in indoor video scenes. Given an image sequence, temporal dependencies of consecutive segmentation fields and spatial dependencies within each segmentation field are unified by a dynamic probabilistic framework based on the conditional random field (CRF). An efficient approximate filtering algorithm is derived for the DCRF model to recursively estimate the segmentation field from the history of observed images. The foreground and shadow segmentation method integrates both intensity and gradient features. Moreover, models of background, shadow, and gradient information are updated adaptively for nonstationary background processes. Experimental results show that the proposed approach can accurately detect moving objects and their cast shadows even in monocular grayscale video sequences.     

交通场景中车辆的运动检测与阴影消除

提出一种算法框架实现对交通场景中运动车辆的分割。首先,提出一种基于颜色空间的浮动气球模型,用以解决监控场景的自适应背景建模问题,该方法解决了基于参数模型的背景建模方法无法检测驻留物体的问题,并可有效适应监控场景中的光照变化以实现自适应更新;其次,针对通过背景建模和背景差分得到的运动前景区域包含运动车辆阴影问题,提出一种新的阴影检测算法,该算法采用多特征融合的方法实现了对运动车辆的分割。实验结果分析表明,与其他方法相比,该算法框架在背景建模和阴影检测方法具有较好的效果。     

圆锥裁剪BLOB的视频目标检测方法

真实场景的视频目标检测需要消除阴影、反射和鬼影等噪声的影响,以检测出运动目标和静止目标.为了实现系统性的视频目标检测,提出一种自适应圆锥裁剪联通块(TC-BLOB)榆测方法.基于BLOB知识,将3D颜色空间变换为"夹角-模差"2D空间后,定义一套圆锥裁剪规则划分出阴影BLOB和反射BLOB;再以一种持久化记忆PM方法判别出鬼影BLOB;最后改进双背景模型检测出静止目标和运动目标.采用不同环境视频进行实验的结果表明,文中方法足有效的,并有独立于后续跟踪的优点.     

A traffic surveillance system using dynamic saliency map and SVM boosting

This paper proposes a traffic surveillance system that can efficiently detect an interesting object and identify vehicles and pedestrians in real traffic situations. The proposed system consists of a moving object detection model and an object identification model. A dynamic saliency map is used for analyzing dynamics of the successive static saliency maps, and can localize an attention area in dynamic scenes to focus on a specific moving object for traffic surveillance purposes. The candidate local areas of a moving object are followed by a blob detection processing including binarization, morphological closing and labeling methods. For identifying a moving object class, the proposed system uses a hybrid of global and local information in each local area. Although the global feature analysis is a compact way to identify an object and provide a good accuracy for non-occluded objects, it is sensitive to image translation and occlusion. Therefore, a local feature analysis is also considered and combined with the global feature analysis. In order to construct an efficient classifier using the global and local features, this study proposes a novel classifier based on boosting of support vector machines. The proposed object identification model can identify a class of moving object and discard unexpected candidate area which does not include an interesting object. As a result, the proposed road surveillance system is able to detect a moving object and identify the class of the moving object. Experimental results show that the proposed traffic surveillance system can successfully detect specific moving objects.     

动态背景下空时特性均显著的运动目标检测

从序列图像中提取变化区域是运动检测的主要作用,动态背景的干扰严重影响检测结果,使得有效性运动检测成为一项困难工作。受静态图像显著性检测启发,提出了一种新的运动目标检测方法,采用自底向上与自顶向下的视觉计算模型相结合的方式获取图像的空时显著性：先检测出视频序列中的空间显著性,在其基础上加入时间维度,利用改进的三帧差分算法获取具有运动目标的时间显著性,将显著性目标的检测视角由静态图像转换为空时性均显著的运动目标。实验和分析结果表明：新方法在摄像机晃动等动态背景中能较准确检测出空时均显著的运动目标,具有较高的鲁棒性。     

多特征组合和图切割支持的物体/背景分割方法

运动物体分割是计算机视觉应用领域中的一个基本问题,阴影和亮度变化均易造成分割结果错误.通过组合多种图像特征,实现了一种新的检测运动物体方法.一方面,组合图像的颜色、梯度和纹理特征,利用梯度和纹理信息时亮度变化不敏感的特性,提高运动物体分割的准确性;另一方面,使用图切割算法对物体/背景进行分割,在不影响整体分割结果前提下修正局部判别错误的像素点,分割结果噪声少且稳定性强.对不同场景的分割结果表明,该方法是高效的和实用的.     

A Competitive Neural Network for Multiple Object Tracking in Video Sequence Analysis

Tracking of moving objects in real situation is a challenging research issue, due to dynamic changes in objects or background appearance, illumination, shape and occlusions. In this paper, we deal with these difficulties by incorporating an adaptive feature weighting mechanism to the proposed growing competitive neural network for multiple objects tracking. The neural network takes advantage of the most relevant object features (information provided by the proposed adaptive feature weighting mechanism) in order to estimate the trajectories of the moving objects. The feature selection mechanism is based on a genetic algorithm, and the tracking algorithm is based on a growing competitive neural network where each unit is associated to each object in the scene. The proposed methods (object tracking and feature selection mechanism) are applied to detect the trajectories of moving vehicles in roads. Experimental results show the performance of the proposed system compared to the standard Kalman filter.     

基于改进动态阈值的运动车辆实时快速检测方法*

提出了复杂交通环境下一种新的运动车辆检测方法。基于背景差分获得运动图像,利用自适应阈值选取方法分别对差分图像的三个颜色通道进行二值化,从而实现运动目标的精确检测。根据检测结果,采用中值更新方法实现背景图像的实时更新。实验结果表明,这种基于改进动态阈值和自适应背景相结合的快速检测算法可以从复杂交通场景图像序列中快速有效地检测出运动目标,能够很好地满足智能交通监控系统中运动车辆实时检测的要求。     

基于视觉背景提取的自适应运动目标提取算法

在复杂场景下的视频运动目标提取是视频分析技术的首要工作。为了解决前景运动目标提取的精确度不高的问题,提出一种基于视觉背景提取(ViBE)的改进视频运动目标提取算法(ViBE⁺)。首先,在背景模型初始化阶段采用像素的菱形邻域来简化样本信息;其次,在前景运动目标提取阶段引入自适应分割阈值来适应场景的动态变化;最后,在更新阶段提出背景重建和调整更新因子方法来处理光照变化的情形。实验结果表明,对于复杂视频场景LightSwitch的运动目标提取结果在相似度指标上,改进后的算法与混合高斯模型(GMM)算法、码本模型算法以及原始ViBE算法相比,分别提高了1.3倍、1.9倍以及3.8倍。所提算法能够在有效时间内对复杂场景具有较好的自适应性,且性能明显优于对比算法。     

一种动态场景多运动目标的综合检测方法

提出一种动态场景下多运动目标检测的方法。该方法融合基于帧间图像差值的运动分割技术以及区域生长法来获得各运动目标的初始轮廓。再利用主动轮廓线模型进行优化，从而得到各运动目标的最优轮廓，该方法具有以下明显特点：允许背景任意复杂；在无补偿情况下仍能得到良好结果；目标大小不影响算法的鲁棒性．实验证明了该方法的有效性、实用性和鲁棒性。     

一种利用时空约束轨迹片段关联的目标跟踪方法*

目标跟踪是智能监控系统中的一个重要的研究领域。由于检测不准确,目标部分或者整体遮挡会造成检测失败。针对以上问题提出一种利用时空约束的轨迹片段关联方法实现对目标的跟踪。首先通过减背景方法检测到移动目标,然后生成轨迹片断,最后通过计算轨迹片段的时空连续性关联轨迹片段,找到最符合时空连续性的轨迹片段关联。通过实验证明方法可以有效解决遮挡,误检测以及目标合并分离问题。     

一种新型的实效运动目标检测方法

提出一种新型的帧间差分光流的运动目标检测方法.该方法通过改进七帧差分和改进背景减除消除运动目标检测时出现的"空洞"和虚假目标;通过在光流计算方程加入权函数和引入通用动态图像模型建立新的光流约束条件,以解决常用光流场计算耗时长和亮度变化引起的约束方程不成立的问题,同时获取运动准确信息;最后通过阈值分割和形态学处理完成对目标的分割.实验对比分析表明,该方法能实现运动目标的准确快速检测与分割.     

动态场景下基于语义和光流约束的视觉同步定位与地图构建

针对动态场景下的定位与静态语义地图构建问题,提出了一种基于语义和光流约束的动态环境下的同步定位与地图构建（SLAM）算法,以降低动态物体对定位与建图的影响。首先,对于输入的每一帧,通过语义分割获得图像中物体的掩模,再通过几何方法过滤不符合极线约束的特征点;接着,结合物体掩模与光流计算出每个物体的动态概率,根据动态概率过滤特征点以得到静态特征点,再利用静态特征点进行后续的相机位姿估计;然后,基于RGB-D图片和物体动态概率建立静态点云,并结合语义分割建立语义八叉树地图。最后,基于静态点云与语义分割创建稀疏语义地图。公共TUM数据集上的测试结果表明,高动态场景下,所提算法与ORB-SLAM2相比,在绝对轨迹误差和相对位姿误差上能取得95%以上的性能提升,与DS-SLAM、DynaSLAM相比分别减小了41%和11%的绝对轨迹误差,验证了该算法在高动态场景中具有较好的定位精度和鲁棒性。地图构建的实验结果表明,所提算法创建了静态语义地图,与点云地图相比,稀疏语义地图的存储空间需求量降低了99%。     

混合视觉系统的运动物体检测和静态地图重建

复杂动态背景环境下的运动物体检测和静态地图重建中容易出现运动物体检测不完整的问题。针对上述问题,提出了一种混合视觉系统下点云分割辅助的运动物体检测方法。首先,提出了直通滤波+随机采样一致性（PassThrough+RANSAC）方法来克服大面积墙壁干扰以实现点云地面点的识别;其次,将非地面点数据作为特征点投射到图像上,并估计其光流运动向量和人工运动向量,从而对动态点进行检测;然后,采用动态阈值策略对点云进行欧氏聚类;最后,整合动态点检测结果与点云分割结果来完整地提取出运动物体。此外,通过八叉树地图（Octomap）工具将点云地图转换为三维栅格地图以完成地图的构建。通过实验结果和数据分析可知,所提方法可以有效提高运动物体检测的完整性,同时重建出低损耗、高实用性的静态栅格地图。     

Contour extraction of moving objects in complex outdoor scenes

This paper presents a new approach to the extraction of the contour of a moving object. The method is based on the fusion of a motion segmentation technique using image subtraction and a color segmentation technique based on the split-and-merge paradigm and edge information obtained from using the Canny edge detector. The advantages of this method are the following: it can detect large moving objects, the background can be arbitrarily complicated and contain many nonmoving objects, and it requires only three image frames that need not be consecutive provided that the moving object is entirely contained in the three frames. It is assumed that there is only one moving object in the image and the objects are not blurred by their motion so that the edges in the image are sharp. The method was applied to road images containing a moving vehicle, and the results show that the contour was correctly extracted in 18 of the 20 cases. We show that this contour extraction method gives good results for other types of moving objects as well. We also describe how the extracted contour can be used to classify a given vehicle into five generic categories. In this study, 19 out of the 20 vehicles were correctly classified. These results demonstrate that integration of multiple cues obtained from relatively simple image analysis techniques leads to a robust extraction of the object of interest in complex outdoor scenes.Research supported by a grant from the U.S. Department of Transportation through the Great Lakes Center for Truck Transportation Research and by a grant from the National Science Foundation (CDA-8806599).