融合特征的卫星视频车辆单目标跟踪

目的 卫星视频作为新兴遥感数据,可以提供观测区域高分辨率的空间细节信息与丰富的时序变化信息,为交通监测与特定车辆目标跟踪等应用提供了不同于传统视频视角的信息。相较于传统视频数据,卫星视频中的车辆目标分辨率低、尺度小、包含的信息有限。因此,当目标边界不明、存在部分遮挡或者周边环境表观模糊时,现有的目标跟踪器往往存在严重的目标丢失问题。对此,本文提出一种基于特征融合的卫星视频车辆核相关跟踪方法。方法 对车辆目标使用原始像素和方向梯度直方图（histogram of oriented gradient,HOG）方法提取包含互补判别能力的特征,利用核相关目标跟踪器分别得到具备不变性和判别性的响应图;通过响应图融合的方式结合两种特征的互补信息,得到目标位置;使用响应分布指标（response distribution criterion,RDC）判断当前目标特征的稳定性,决定是否更新跟踪器的表征模型。本文使用的相关滤波方法具有计算量小且运算速度快的特点,具备跟踪多个车辆目标的拓展能力。结果 在8个卫星视频序列上与主流的6种相关滤波跟踪器进行比较,实验数据涵盖光照变化、快速转弯、部分遮挡、阴影干扰、道路颜色变化和相似目标临近等情况,使用准确率曲线和成功率曲线的曲线下面积（area under curve,AUC）对车辆跟踪的精度进行评价。结果表明,本文方法较好地均衡了使用不同特征的基础跟踪器（性能排名第2）的判别能力,准确率曲线AUC提高了2.9%,成功率曲线AUC下降了4.1%,成功跟踪车辆目标,不发生丢失,证明了本文方法的先进性和有效性。结论 本文提出的特征融合的卫星视频车辆核相关跟踪方法,均衡了不同特征提取器的互补信息,较好解决了卫星视频中车辆目标信息不足导致的目标丢失问题,提升了精度。     

Presenting a traffic management and control system in driver assistance form based on vehicular networks

Vehicular Networks is considered a major step in the field of Intelligent Transportation System (ITS). In this technology, some equipment will be installed on vehicles and special places at roadsides which will enable the wireless communication between vehicles with each other and will provide the communication between the vehicles and roadside equipment. One of the ITS application is Traffic monitoring system. Such system enables accessing traffic videos by traffic monitoring centers to make traffic decision. However, providing traffic video for the vehicles can be appealing. This paper addresses a new application in vehicular networks and ITS which can provide this videos for drivers in a city. Each driver request timely traffic video of a location from a web server and the web server forward this request to a stream management server. This server based on current location of the requester vehicle, its speed and its direction calculates appropriate video chunks for each RSU along vehicle destination. This study aims to present a system which can bring a high accessibility for content and can provide it with an appropriate bandwidth and quality for vehicles. Due to the scalability and bandwidth limitations for its content and streaming, vehicular networks are used in this system.     

Trajectory clustering in CCTV traffic videos using probability product kernels with hidden Markov models

This article presents a method for clustering the trajectories obtained by tracking vehicles in traffic videos, recorded from CCTV cameras in public spaces. The proposed method employs a model-based approach in which (1) each trajectory (position and velocity) is modelled using a hidden Markov model (HMM), and (2) the distance between two trajectories is computed as the probabilistic similarity between HMMs, by means of the probability product kernel. Experiments on a set of real traffic video sequences reveal very good results of the proposed approach, outperforming two non-trivial baselines. To the best of the authors’ knowledge, this approach is novel for trajectory grouping in traffic videos.     

运动目标三维轨迹可视化与关联分析方法

随着治安监控系统的普及,越来越多的监控摄像头被安装在各个交通道路和公共场所中,每天都产生大量的监控视频.如今,监控视频分析工作主要是采用人工观看的方式来排查异常,以这种方式来分析视频内容耗费大量的人力和时间.目前,关于视频分析方面的研究大多是针对目标个体的异常行为检测和追踪,缺乏针对对象之间的关联关系的分析,对视频中的一些对象和场景之间的关联关系等还没有较为有效的表示和分析方法.针对这一现状,提出一种基于运动目标三维轨迹的关联视频可视分析方法来辅助人工分析视频,首先对视频资料进行预处理,获取各个目标对象的运动轨迹信息,由于二维轨迹难以处理轨迹的自相交、循环运动和停留等现象,并且没有时间信息就难以对同一空间内多个对象轨迹进行的关联性分析,于是结合时间维度对轨迹进行三维化扩展.该方法支持草图交互方式来操作,在分析过程中进行添加草图注释来辅助分析.可结合场景和对象的时空关系对轨迹进行关联性计算,得出对象及场景之间的关联模型,通过对对象在各个场景出现状况的统计,结合人工预先设定的规则,可实现对异常行为报警,辅助用户决策.     

三焦点张量重投影视频稳像算法

目的 目前,特征点轨迹稳像算法无法兼顾轨迹长度、鲁棒性及轨迹利用率,因此容易造成该类算法的视频稳像结果扭曲失真或者局部不稳。针对此问题,提出基于三焦点张量重投影的特征点轨迹稳像算法。方法 利用三焦点张量构建长虚拟轨迹,通过平滑虚拟轨迹定义稳定视图,然后利用三焦点张量将实特征点重投影到稳定视图,以此实现实特征点轨迹的平滑,最后利用网格变形生成稳定帧。结果 对大量不同类型的视频进行稳像效果测试,并且与典型的特征点轨迹稳像算法以及商业软件进行稳像效果对比,其中包括基于轨迹增长的稳像算法、基于对极几何点转移的稳像算法以及商业软件Warp Stabilizer。本文算法的轨迹长度要求低、轨迹利用率高以及鲁棒性好,对于92%剧烈抖动的视频,稳像效果优于基于轨迹增长的稳像算法;对于93%缺乏长轨迹的视频以及71.4%存在滚动快门失真的视频,稳像效果优于Warp Stabilizer;而与基于对极几何点转移的稳像算法相比,退化情况更少,可避免摄像机阶段性静止、摄像机纯旋转等情况带来的算法失效问题。结论 本文算法对摄像机运动模式和场景深度限制少,不仅适宜处理缺少视差、场景结构非平面、滚动快门失真等常见的视频稳像问题,而且在摄像机摇头、运动模糊、剧烈抖动等长轨迹缺乏的情况下,依然能取得较好的稳像效果,但该算法的时间性能还有所不足。     

Remote sensing from space: the European Space Agency's achievements and current programmes

Abstract

The European Space Agency (ESA) has made a significant contribution in the field of remote sensing from space, beginning in 1972 in the field of weather forecasting with the development of its preoperational meteorological satellites ‘Meteosat’. The launch on 6 March 1989 of the first operational Meteosai, which will be followed by the launch of two more satellites, will provide continuity of service until the mid 1990s. This will be followed by the second generation Meteosat which will be developed in partnership with EUMETSAT. Following The Hague Ministerial Council of November 1987, the Agency now has endorsement of a long term plan for Earth observation building upon the European Remote Sensing Satellite (ERS-1) programme: to be followed by a similar climate monitoring satellite which will lead in turn to the era of the Polar Platform.     

Faster CNN-based vehicle detection and counting strategy for fixed camera scenes

Automatic detection and counting of vehicles in a video is a challenging task and has become a key application area of traffic monitoring and management. In this paper, an efficient real-time approach for the detection and counting of moving vehicles is presented based on YOLOv2 and features point motion analysis. The work is based on synchronous vehicle features detection and tracking to achieve accurate counting results. The proposed strategy works in two phases; the first one is vehicle detection and the second is the counting of moving vehicles. Different convolutional neural networks including pixel by pixel classification networks and regression networks are investigated to improve the detection and counting decisions. For initial object detection, we have utilized state-of-the-art faster deep learning object detection algorithm YOLOv2 before refining them using K-means clustering and KLT tracker. Then an efficient approach is introduced using temporal information of the detection and tracking feature points between the framesets to assign each vehicle label with their corresponding trajectories and truly counted it. Experimental results on twelve challenging videos have shown that the proposed scheme generally outperforms state-of-the-art strategies. Moreover, the proposed approach using YOLOv2 increases the average time performance for the twelve tested sequences by 93.4% and 98.9% from 1.24 frames per second achieved using Faster Region-based Convolutional Neural Network (F R-CNN ) and 0.19 frames per second achieved using the background subtraction based CNN approach (BS-CNN ), respectively to 18.7 frames per second.

     

An efficient and flexible approach for multiple vehicle tracking in the aerial video sequence

ABSTRACT

Multiple vehicle tracking (MVT) in the aerial video sequence can provide useful information for the applications such as traffic flow analysis. It is challenging due to the high requirement for the tracking efficiency and variable number of the vehicles. Furthermore, it is particularly challenging if the vehicles are occluded by the shadow of the trees, buildings, and large vehicles. In this article, an efficient and flexible MVT approach in the aerial video sequence is put forward. First, as the pre-step to approach the MVT problem, the superpixel segmentation-based multiple vehicle detection (MVD) is achieved by combining the two-frame difference and superpixel segmentation. The two-frame difference is utilized to reduce the search space. By scanning the search space via the centre of the superpixels, the moving vehicles are detected efficiently. Then, the deterministic data association is proposed to tackle the MVT problem. To improve the tracking accuracy, we fuse multiple types of features to establish the cost function. With respect to the variable number of the vehicles, the tracker management is designed by establishing or deleting the trackers. Furthermore, for the occlusion handling, we focus on the accurate state estimation, and it is realized by the driver behaviour-based Kalman filter (DBKF) method. In the DBKF method, we take seriously into account the driver behaviour, including the speed limit and rear-end collision avoidance with the front vehicle. Both tracker management and occlusion handling make the MVT approach flexibly cope with varieties of traffic scenes. Finally, comprehensive experiments on the DARPA VIVID data set and KIT AIS data set demonstrate that the proposed MVT algorithm can generate satisfactory and superior results.     

交通路口视频奇异运动事件检测

提出了一种基于运动特征的交通路口视频奇异事件的检测的方法。该方法针对具体应用背景,不需要进行单个目标的跟踪和检测,而是直接运动运动特征进行检测。方法首先提取运动像素,并通过运动像素集合和运动矢量的大小判断运动目标种类（车辆或行人）。然后,在此基础上进一步分析目标的运动轨迹,将新出现的和已经有运动轨迹明显不一致的运动,和明显不符合交通规则的运动（如车辆的逆行）判定为异常运动。该方法在一定样本基础上进行了实验,实验结果表明,该方法可以较好地检测交通路口的奇异性运动事件。     

OBCD-HH: an object-based change detection approach using multi-feature non-seed-based region growing segmentation

There is an increasing need to get updated information regarding the changes on earth’s surface. The information obtained can be used in a wide range of applications including disaster management, land-use investigation etc. The high-resolution remote sensing images obtained from satellites provide us with an opportunity to detect changes on earth’s surface between various time intervals. In this paper, an unsupervised object-based change detection (OBCD) method is proposed to detect changes in high resolution bi-temporal satellite images. To detect changes, a novel multi-feature non-seed-based region growing (MF-NSRG) algorithm is proposed for image segmentation based on heterogeneity minimization that uses textural heterogeneity along with spectral and spatial heterogeneity during region growing. The performance of MF-NSRG algorithm is further improved by using Harris Hawk, a recently proposed metaheuristic algorithm, which is used to obtain optimal values of segmentation parameters. Finally, the feature maps extracted from the pre-change and post-change segmented images are analysed using histogram trend similarity (HTS) approach to detect changes. The proposed approach is known as object-based change detection using Harris Hawk (OBCD-HH). The proposed OBCD-HH approach is applied on two datasets: xBD and Onera Satellite Change Detection (OSCD) dataset. Its performance is compared with existing state-of-the-art algorithms and results show the superiority of the proposed approach.

     

一种面向无人机视频的多尺度摘要的设计与实现

无人机视频是利用无人机航拍得到的一类重要的视频资源,被广泛运用于地面目 标的监测。但是,无人机视频的视野辽阔、不具有目标针对性的拍摄特点,使其存在大量时空 冗余,传统的视频交互手段显得十分低效。为此,提出了一种面向无人机视频的多尺度螺旋摘 要。首先,基于 YOLOv3 算法,训练能检测无人机视角的行人、车辆等目标的模型。然后,提 出了基于关键帧的视频目标检测算法,根据改进后的基于颜色特征的关键帧提取算法提取涵盖 视频关键信息的关键帧,并将检测模型应用于关键帧,高效获取整个视频的目标检测结果。之 后,从关键帧中提取相应的关键区域,作为摘要的呈现单元,并以螺旋的形式从内向外地将摘 要单元逐一呈现,辅以基于关键帧的视频定位和尺度缩放功能。最后,开发了草图注释、目标 分布螺旋、双螺旋播放等新颖的交互工具,满足用户的潜在需求,共同实现面向无人机视频的 高效交互。     

Satellite detection of tropical burning in Brazil

Abstract

Tropical burning often occurs in remote areas of the world. Satellite remote sensing is the only practical solution for detecting and monitoring this burning. In this paper we demonstrate the capability of the Advanced Very High Resolution Radiometer onboard the National Oceanic and Atmospheric Administration polar orbiting satellites for detecting tropical fire activity in the Manaus, Brazil area.     

基于HEVC的车辆异常事件检测

当前传统交通事故检测和查阅主要通过人工监测的方法,这种方法效率低且实时性差,本文提出一种基于最新压缩域视频编码标准HEVC（High-efficiency video coding）的车辆异常事件检测方法。首先对HEVC码流中提取出的运动矢量信息进行运动矢量累积迭代和中值滤波的预处理,之后根据提取出的块划分信息和运动矢量信息计算运动对象的运动强度,然后根据运动强度值和八连通区域法提取出运动对象,最后根据空间距离法和运动强度判别法检测出视频序列中发生的车辆异常事件。实验证明,该方法可以准确地检测出视频序列中发生的车辆异常事件;对于有着快速移动的运动目标以及多个运动目标的视频效果更好。     

智能交通检测系统中动态目标检测方法研究

视频交通检测技术已逐渐成为交通信息采集领域的主流技术。以交通信息采集系统中运动车辆的检测与识别为应用背景,在对运动目标检测中背景更新、噪声的消除等一些难点问题进行深入分析研究的基础上,给出了一个稳定的运动车辆检测算法。运动车辆检测方法作为视觉监控领域的一种普遍方法,具有一定的理论意义和实用价值。     

Representation of occurrences for road vehicle traffic

Our 3D-model-based Computer Vision subsystem extracts vehicle trajectories from monocular digitized videos recording road vehicles in inner-city traffic. Steps are documented which import these quantitative geometrical results into a conceptual representation based on a Fuzzy Metric-Temporal Horn Logic (FMTHL, see [K.H. Schäfer, Unscharfe zeitlogische Modellierung von Situationen und Handlungen in Bildfolgenauswertung und Robotik, Dissertation, 1996]). The facts created by this import step can be understood as verb phrases which describe elementary actions of vehicles in image sequences of road traffic scenes. The current contribution suggests a complete conceptual representation of elementary vehicle actions and reports results obtained by an implementation of this approach from real-world traffic videos.     

Moving objects forecast in image sequences using autoregressive algorithms

The objective of this paper is to present an overall approach to forecasting the future position of the moving objects of an image sequence after processing the images previous to it. The proposed method makes use of classical techniques such as optical flow to extract objects’ trajectories and velocities, and autoregressive algorithms to build the predictive model. Our method can be used in a variety of applications, where videos with stationary cameras are used, moving objects are not deformed and change their position with time. One of these applications is traffic control, which is used in this paper as a case study with different meteorological conditions to compare with.

基于光流法的卫星视频交通流参数提取研究

抽样分辨率达1米的高清卫星视频已经能够实现对地面较小的运动目标的实时监控。针对卫星视频中运动车辆目标仅显示为一个或几个像素点的特点,提出了一种基于光流法的卫星视频交通流参数提取的思路与方法。该方法利用卫星视频中车辆目标为像素点的特点,结合Shi-Tomasi角点检测方法实现车辆检测及车辆计数;在车辆检测的基础上利用光流法得到的连续视频帧中角点的位置信息进行双向车辆平均车速的计算,并对实验结果进行了对比分析。该文是基于卫星视频中小微运动车辆目标进行交通流参数提取的一次有益尝试。     

利用七宫格的遮挡车辆凹性检测与分割

目的:交通场景中车辆间的距离过近或相互遮挡容易造成识别上的粘连,增加了准确检测目标车辆的难度,因此需要建立有效、可靠的遮挡车辆分割机制。方法:首先在图像分块的基础上确定出车辆区域,根据车辆区域的长宽比和占空比进行多车判断;然后提出了一种基于“七宫格”的凹陷区域检测算法,用以找出车辆间的凹陷区域,通过匹配对应的凹陷区域得到遮挡区域;最后,将遮挡区域内检测出的车辆边缘轮廓作为分割曲线,从而分割遮挡车辆。结果:实验结果表明,在满足实时性的前提下算法具有较高的识别率,且能够按车辆的边缘轮廓准确分割多个相互遮挡的车辆。与其他算法相比,该算法提高了分割成功率和分割精度,查全率和查准率均可达到90%。结论:提出了一种新的遮挡车辆分割方法,有效解决了遮挡车辆不宜分割和分割不准确的问题,具有较强的适应性。     

国内外赤潮卫星遥感技术与应用进展

卫星遥感已成为赤潮探测与监测的重要技术手段之一。回顾了国内外赤潮卫星遥感技术与应用的进展,对国内外已开发的赤潮卫星遥感技术与方法进行了简要的评述,并给出了国内利用NOAA和SeaStar卫星探测与监测赤潮的3个实例。讨论了我国赤潮卫星遥感存在的问题,提出了发展我国赤潮卫星遥感技术与应用的建议。     

基于多源卫星遥感的灾害性气象观测数据监测系统设计

灾害性气象的频发不仅给人们的生活带来了极大的影响,而且还危及生态环境的稳定。为减小实测气象数据与真实数据样本之间误差,实现对灾害性气象观测数据的准确监测,设计基于多源卫星遥感的灾害性气象观测数据监测系统。设置温湿度传感器及防辐射罩,按照数据信息传输网络布局形式,将风速风向传感器、雨量传感器、太阳能控制器接入既定模块单元之中,完成监测系统的硬件设计。利用多源卫星遥感技术,定义多源影像空间,通过分解数字卫星图像的方式,确定监测极值点所处位置,并计算相似性度量指标的具体数值,完成基于多源卫星遥感的灾害性气象图像配准处理。根据上位机组网模型,完善WINSOCK控件的连接形式,实现监测系统的执行程序编制,联合相关传感器元件,完成基于多源卫星遥感的灾害性气象观测数据监测系统设计。实验结果表明,多源卫星遥感技术设计系统的湿度测量曲线、气压测量曲线、风速测量曲线与真实气象数据样本之间的误差值均未超过2%,能够准确监测灾害性气象观测数据。