188金宝搏车辆实际速度将显示在视频中的车辆旁边,提出了一种快速检测图像中所有可能车辆的技术

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本文为澳大利亚西澳大学(作者:Soo Siang Teoh)的博士论文,共171页。

本文为加拿大滑铁卢大学(作者:KURTIS NORMAN
MCBRIDE)的硕士论文,共80页。

本文研究了基于单目摄像机的车辆检测技术,提出了一种能在视频帧中对车辆运动进行可靠检测和跟踪的新系统。该系统由三个主要模块组成:基于对称的车辆提示目标检测、用于车辆验证的两类支持向量机分类器和基于卡尔曼滤波器的车辆跟踪器。

车辆跟踪是目标检测和跟踪的关键应用之一。交通监控在如今这个时代已经变得至关重要,目前,道路上的车辆数量已经大大增加。为了保护驾驶员的安全,交通执法部门在全国各地的路段都指定了限速。然而,不负责任的司机仍然会超过限速,因为他们知道自己不太可能被抓到并接收惩罚。本文开发了一种能够在视频中检测运动车辆并显示行驶速度的系统。如果车辆超过允许的速度限制,车辆实际速度将显示在视频中的车辆旁边,以便交通执法人员能够根据显示的速度采取必要的措施。该系统以Matlab/Simulink为仿真平台,为阈值、滤波和Blob分析提供了综合的工具。光流(optical
flow)是用来确定运动车辆的图像处理技术,采用中值滤波方法去除阈值图像中的椒盐噪声。多种形态学技术的组合被用来矫正处理后的图像,Blob分析在运动目标周围生成矩形框。矩形框的质心用于确定每辆车在给定框架上的位置。为了弥补深度感知信息的缺失,摄像头的高度和与道路之间的夹角是固定的,因此可以确定车辆接近摄像头的速度。结果表明,该系统能够成功地检测到车辆并显示其速度,且显示的速度误差相对较小。显示速度设置为每两帧更改一次,以便更容易进行观察。

在目标提示阶段,提出了一种快速检测图像中所有可能车辆的技术。这项技术利用了这样一个事实:大多数车辆的前后视图在水平轴上高度对称。首先,利用多尺度对称搜索窗口提取图像中的对称区域和高对称点,然后对高对称点进行聚类,并利用每个聚类的平均位置来假设潜在车辆的位置。通过研究发现,在缩小后的图像上沿多条扫描线进行稀疏对称搜索,可以在不牺牲检测概率的情况下显著缩短处理时间。

Vehicle tracking is one of the critical applications of object detection
and tracking. Traffic surveillance has become crucial in this day and
age where the number of vehicles on the road has risen considerably. To
preserve the safety of motorists, traffic law enforcement assign speed
limits at different locations throughout the country. However,
irresponsible motorists still exceed the speed limit since they know it
is unlikely that they will get caught. In this paper, a system is
developed which is capable of detecting moving vehicles in a video and
display the vehicles speed as it goes. Should a vehicle exceed the
allowed speed limit, it will be displayed in the video alongside the
vehicle so that traffic law enforcers will be able to take necessary
action based on the displayed speed. The system uses Matlab/Simulink as
a simulation platform as it provides comprehensive tools for
thresholding, filtering and blob analysis. Optical flow was the image
processing technique used to determine the moving vehicles. A median
filter was used to remove salt and pepper noise from the thresholded
image. Combinations of several morphological operations were used to
rectify whatever that is left. Blob analysis produces rectangles around
the moving objects. The centroid of the rectangle is used to determine
the location of each vehicle at a given frame. To make up for the
absence of depth perception, the camera’s height and angle from the road
is fixed so that the rate of which a vehicle approaches the camera can
be determined. The results show that the system successfully detects
vehicles and displays its speed, though there is a relatively small
margin of error for the displayed speed. The displayed speed is set to
only change once every couple of frames so that it would be easier to
see.

需要通过车辆验证来消除提示阶段发现的错误检测目标。研究了基于模板匹配和图像分类的几种验证技术,并对图像特征和分类器的不同组合进行了性能评价。研究结果表明,基于SVM分类器训练的定向梯度特征直方图在合理的处理时间内具有最佳的性能。

1.1车辆跟踪与速度估计简介

系统的最后一个阶段是车辆跟踪。本文提出了一种基于卡尔曼滤波器和可靠点系统的跟踪功能,该功能可以跟踪连续视频帧中检测到的车辆运动和尺寸变化。

3.3将RGB转换为强度信息

该系统由上述三个模块集成实现,从而为单目车辆检测提供了一种新的解决方案。实验结果表明,该系统能在不同的天气条件下,有效地检测公路和复杂城市道路上的多辆汽车。

3.8输出视频的目标速度显示

This dissertation investigates thetechniques for monocular-based vehicle
detection. A novel system that canrobustly detect and track the movement
of vehicles in the video frames isproposed. The system consists of three
major modules: a symmetry based objectdetector for vehicle cueing, a
two-class support vector machine classifier for vehicle verification and
a Kalman filter based vehicle tracker.For the cueing stage, a technique
for rapid detection of all possible vehiclesin the image is proposed.
The technique exploits the fact that most vehicles’front and rear views
are highly symmetrical in the horizontal axis. First, it extractsthe
symmetric regions and the high symmetry points in the image using
amulti-sized symmetry search window. The high symmetry points are then
clusteredand the mean locations of each cluster are used to hypothesize
the locations ofpotential vehicles. From the research, it was found that
a sparse symmetrysearch along several scan lines on a scaled-down image
can significantly reducethe processing time without sacrificing the
detection rate. Vehicleverification is needed to eliminate the false
detections picked up by thecueing stage. Several verification techniques
based on template matching andimage classification were investigated.
The performance for differentcombinations of image features and
classifiers were also evaluated. Based onthe results, it was found that
the Histogram of Oriented Gradient featuretrained on the SVM classifier
gave the best performance with reasonableprocessing time. The final
stage of the system is vehicle tracking. A trackingfunction based on the
Kalman filter and a reliability point system is proposedin this
research. The function tracks the movement and the changes in size ofthe
detected vehicles in consecutive video frames. The proposed system
isformed by the integration of the above three modules. The system
provides anovel solution to the monocular-based vehicle detection.
Experimental resultshave shown that the system can effectively detect
multiple vehicles on thehighway and complex urban roads under varying
weather conditions.

4.1整个项目的Simulink模型

188金宝搏,1引言

2基于视觉的车辆检测技术综述

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3一种创新的基于对称性的车辆提示技术

4基于分类器的车辆验证技术

5基于卡尔曼滤波器的车辆跟踪

6系统集成、实验与结果

7结论与未来工作展望

附录A第四章实验的特征向量文件格式

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