标题: 利用架设在视讯监控车上成对的全方位摄影装置作周遭环境监控之研究
A Study on Surrounding Environment Monitoring by a Video Surveillance Car with Two 2-camera Omni-imaging Devices
作者: 陈俊甫
Chen, Chun-Fu
蔡文祥
Tsai, Wen-Hsiang
资讯科学与工程研究所
关键字: 光流分析法;全方位摄影装置;车辆侦测;optical flow;omni-camera;car detection
公开日期: 2010
摘要: 本研究利用架设在一视讯监控车顶上的两组全方位摄影装置来达到视讯监控的功能,主要应用于监控行车视角的盲点和周遭的车辆。
在本研究中,此二全方位摄影机装置可用以监看车辆周遭任何角度的影像画面。此外,本研究利用光流分析法直接套用在连续撷取的影像上,并利用影像的移动向量分析目前车辆的行走方向,产生对应方位的透视影像(perspective-view image),方便驾驶观看。另一方面,本研究亦提出一种“透视对应表”(perspective mapping table),可以快速地将全方位影像转成透视影像,提供驾驶观看行车纪录。
同时,本研究利用全方位摄影系统所拍摄的影像,可静态监控周遭静止的车辆并求得立体资讯。另利用影像处理技术取出影像中的车体区域,并侦测车窗底缘的对应点,计算车辆位置,进而产生监控车周遭环境的俯视图。
除了侦测静态的车辆,本研究也提出了行驶当中的视讯监控车侦测到停止或移动的周遭车辆的方法。另亦使用光流分析法,配合全方位摄影机所撷取到的连续影像,利用有高度的物体会产生较大移动向量的性质,将车体给大致分割出来,进而使用“k均值分群法”(k-means clustering)去侦测出车体,接着透过区域增长法去找出较完整的车体,最后再利用预先造好的车辆模型去做比对,藉以取得周遭车辆的位置资讯,划出车辆周遭的俯视图,供车辆驾驶观看。
上述方法的实验结果皆甚良好,显示所提视讯监控系统确实可行。
In this study, methods are proposed for video surveillance by a video
surveillance vehicle equipped with a pair of two-camera omni-imaging devices on its roof, with emphasis on monitoring of blind spots and nearby cars around the vehicle.
First, for generating perspective-view images to facilitate inspection of the vehicle’s surrounding environment, a space-mapping table and an r-rho mapping table are created to accelerate the related coordinate transformation process. Also, a method
for generating the perspective-view image of the surrounding area of the vehicle by estimating the vehicle’s moving direction using optical flow analysis is proposed. For off-line inspection of the driving history, a method of using a perspective-mapping table proposed in this study to generate a series of perspective-view images of any view direction decided by mouse clicking is proposed as well.
Furthermore, a method for monitoring a nearby static car around the surveillance vehicle is proposed, which employs image processing and pattern recognition techniques like ground region elimination, moment-preserving thresholding, region growing, etc. to segment a car shape out of the omni-image. Also proposed is a method for extracting the bottom-edge points of the car window and eliminating the outlier points by simple linear regression, in order to compute the 3D data of the detected car and generate a surround map.
In addition, a method for monitoring a nearby static or moving car from a moving video surveillance vehicle is proposed, which may be used to segment the nearby car region in the omni-image by the use of motion vector lengths. To further grow a complete car shape from the segmented regions, a method for finding the pixels of the car body by a k-means algorithm and using the pixels as seed points to grow the entire car region by the use of color information is also proposed. With the aid of a space-mapping table, car masks derived from a simple car model are used for locating the position of the detected car. Finally, a top-view surround map showing the relative position of the detected car with respect to the video surveillance vehicle
is generated.
Good experimental results are also shown, which prove the feasibility of the proposed methods for real video surveillance applications.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT079855594
http://hdl.handle.net/11536/48329
显示于类别:Thesis


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