复杂环境中交通标识识别与状态跟踪估计算法研究
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摘要
自21世纪初以来,许多研究者和汽车制造商均在无人驾驶车技术的研究与开发投入了大量人力和物力。其中一个重要研究内容是基于计算机视觉的道路环境感知：识别车辆行驶道路周边的交通标志信息和交通信号灯状态,为无人驾驶车行驶提供决策依据。
在国内外已有的交通标识检测,识别,跟踪和状态估计方面的研究成果基础上,结合交通标识系统在无人驾驶车上的实际应用和测试性能,设计并构建了面向无人驾驶车的交通标识实时检测,识别和跟踪的算法和系统。主要研究工作和创新性成果如下：
介绍了相机的成像过程,根据相机CCD的原始参数及标定的相机内参数,估计出道路环境中交通标识与车载相机间的近似距离与角度。为了控制相机的曝光时间,调节图像亮度,需预先判断交通标识出现区域的亮度信息和曝光量,根据曝光情况选择不同的权值矩阵,计算出最佳曝光时间。该方法能准确地调节相机的曝光时间,以采集到亮度合适的图像,适合于交通标识的检测与识别处理。
提出了50类符号型以及多种文字型的交通标志的检测与识别算法。变换RGB空间图像,突出交通标志的特征颜色(红色,黄色,蓝色),选择合适的阈值分割图像。重构形态学处理后的交通标志感兴趣区域边缘,降低误检率。选择形状的标记图特征以分类感兴趣区域的形状并排除干扰。对文字型交通标志,则用阈值分割图像中的墨绿色和蓝色区域,判断区域的形态,选择矩形区域作为交通标志候选。将候选区域的二值图像先向水平,后向垂直方向投影,用三次样条插值法拟合该投影曲线,定位曲线的波谷,确定文字的行和列位置,以分割出单个文字区域。用两种模型表示方法：(1)二元树复小波变换和二维独立分量分析方法；(2)基于内部图形的模板匹配分别识别候选区域的交通标志类型,然后用决策规则融合两种方法的识别结果,并排除干扰区域。实验结果表明交通标志的识别率超过91%,平均处理时间为171ms,所提出的交通标志识别算法性能优越,适合应用于无人驾驶车辆的环境感知系统。
提出了箭头型和组合型交通信号灯的检测和识别算法。对于箭头型交通信号灯,根据灯板和信号灯的颜色和形态特征,定位出图像中的灯板及交通信号灯位置。对于组合型交通信号灯,则先将图像进行TopHat变换,然后将其从RGB空间转换到YCbCr空间,并进行阈值分割和形态学滤波。根据区域的宽高,面积,占空比等形态信息初步过滤。组合感兴趣区域,向水平方向投影,根据波谷位置定位单个交通信号灯区域。将候选区域灰度化,归一化,提取Gabor小波特征,用2维独立分量分析方法降低特征的冗余度,送入最近邻分类器以判断交通信号灯的状态。在3个城市内采集了大量的视频,测试了算法性能,综合性能表明该算法的总体识别率在91%以上,平均处理时间为152ms,能实时,稳定,准确的识别交通信号灯。
构建了交通标志的多目标跟踪模型,定义了交通标志目标的状态。用无迹卡尔曼滤波算法建立单个交通标志目标的状态和运动轨迹模型,预测交通标志目标的位置。而对交通信号灯,则用Kalman滤波跟踪交通信号灯的灯板和灯区域。选择观察序列训练单个和三个交通信号灯目标的隐马尔科夫模型参数,用隐马尔科夫模型算法估计交通信号灯下一时刻的状态信息。
建立了交通标识识别系统的实验平台,设计和实现了交通标识识别的两个子系统：(1)交通标志识别系统；(2)交通信号灯识别系统。详细描述了系统的功能,显示形式,参数设置等。
Since the beginning of the21st century, many researchers and manufacturers invested a lot of human and material resources in the research and development of unmanned vehicle's technology. Road condition perception based on computer vision is one of the important researched contents:recognition on the information of traffic sign and the state of traffic light in both sides of the road. It will provide the decision-making basis for driving the unmanned vehicle.
On the basis of existing research results in the detection, recognition and tracking of the traffic signs at home and abroad, combining the practical application and test performance of the traffic recognition system in the unmanned vehicle, the real-time system of traffic sign recognition and tracking for the unmanned vehicle was designed and constructed. The main research work and achievements are as following:
An imaging process of camera was introduced here. According to the original parameters of CCD(Charge Coupled Device) and intrinsic parameters by the camera calibration, the approximate distance and angle between the traffic sign, and the position of camera on the vehicle are estimated in the road environment. In order to control the exposure time of the camera and adjust the brightness of the image, the brightness information and the exposure value of region which the traffic sign appeared are judged in advance. Different weight matrice are chosen by the exposure of image to calculate the best exposure time. The proposed method can accurately adjust the exposure time of the camera and get image which has appropriate brightness. It is suitable for detection and recognition of the traffic sign.
The detection and recognition algorithm for50symbolic and text traffic signs are presented. RGB value of the image is transformed to highlight the characteristic colors(red, yellow, blue) of the traffic signs. The appropriate thresholds are selected for the image segmentation. The edge of traffic sign's ROI(region of interest) is reconstructed to decrease the error rate. The shapes of traffic sign's ROI are classified and excluded the interference by the signature. For text traffic sign, atrovirens and blue regions are segmented by the thresholds. The regional morphology is judged, and the rectangle region is selected as a traffic sign candidate. The binary image of the candidate region projects to horizontal and vertical direction. The projection curve is fitted by cubic spline interpolation; the curve peak is located to determine the positions of the row and column to separates the text region. Two model representation methods:(1)two dual-tree complex wavelet transform and two-dimensional independent component analysis;(2)template matching based on the internal graphics recognize the traffic sign's type of candidate region respectively. Then the two recognition results are fused and excluded by decision rules. The experimental results show that the recognition rate of traffic sign is more than91%, and the average processing time is171ms. The proposed algorithm has superior performance and is suitable for the sensing system on unmanned vehicle.
Traffic light detection and recognition algorithms for arrow and combination type are proposed. For the arrow traffic light, the position of the board and lamp of traffic lights are located by the color and morphological features of the board and lamp. For the combination of traffic light, the image is transformed by TopHat and convered to YCbCr from RGB space firstly. Then it is segmented by the threshold and filtered by morphology. The regions are preliminarily filtered according to the morphological information such as width, height, area, and duty radio. The ROIs are combined and projected to the horizontal direction. The single region of traffic light is located by the valley. In the traffic light recognition process, the candidate region image is grayscaled and normalized. Feature is extracted by the Gabor wavelet and reduced the redundancy by2dimensional independent component analysis. Feature is sent into the nearest neighbor classifier to judge direction information of the traffic light. Many videoes are collected in3cities and tested algorithm performance. Comprehensive performances show that overall recognition rate of the algorithm is more than91%and the average processing time is152ms. It achieves real-time, stable, accurate goal to recognize traffic light.
The multi-target tracking model of the traffic sign is established. And the target state of the traffic sign is defined. The target state and trajectory model of one traffic sign is established by using unscented Kalman filter to predict the position of traffic sign target. For the traffic light, the board and lamp of the traffic light are tracking by the Kalman filter. Observation sequences are selected to train Hidden Markov Model parameters for one and three traffic light. The next state is estimated by the hidden Markov model algorithm.
The experimental platform of traffic sign recognition system is established. Two subsystems of traffic sign recognition are designed and implemented:(1) the traffic sign recognition system;(2) the traffic light recognition system. It detailedly performs the system function, display form and parameter setting.
在国内外已有的交通标识检测,识别,跟踪和状态估计方面的研究成果基础上,结合交通标识系统在无人驾驶车上的实际应用和测试性能,设计并构建了面向无人驾驶车的交通标识实时检测,识别和跟踪的算法和系统。主要研究工作和创新性成果如下：
介绍了相机的成像过程,根据相机CCD的原始参数及标定的相机内参数,估计出道路环境中交通标识与车载相机间的近似距离与角度。为了控制相机的曝光时间,调节图像亮度,需预先判断交通标识出现区域的亮度信息和曝光量,根据曝光情况选择不同的权值矩阵,计算出最佳曝光时间。该方法能准确地调节相机的曝光时间,以采集到亮度合适的图像,适合于交通标识的检测与识别处理。
提出了50类符号型以及多种文字型的交通标志的检测与识别算法。变换RGB空间图像,突出交通标志的特征颜色(红色,黄色,蓝色),选择合适的阈值分割图像。重构形态学处理后的交通标志感兴趣区域边缘,降低误检率。选择形状的标记图特征以分类感兴趣区域的形状并排除干扰。对文字型交通标志,则用阈值分割图像中的墨绿色和蓝色区域,判断区域的形态,选择矩形区域作为交通标志候选。将候选区域的二值图像先向水平,后向垂直方向投影,用三次样条插值法拟合该投影曲线,定位曲线的波谷,确定文字的行和列位置,以分割出单个文字区域。用两种模型表示方法：(1)二元树复小波变换和二维独立分量分析方法；(2)基于内部图形的模板匹配分别识别候选区域的交通标志类型,然后用决策规则融合两种方法的识别结果,并排除干扰区域。实验结果表明交通标志的识别率超过91%,平均处理时间为171ms,所提出的交通标志识别算法性能优越,适合应用于无人驾驶车辆的环境感知系统。
提出了箭头型和组合型交通信号灯的检测和识别算法。对于箭头型交通信号灯,根据灯板和信号灯的颜色和形态特征,定位出图像中的灯板及交通信号灯位置。对于组合型交通信号灯,则先将图像进行TopHat变换,然后将其从RGB空间转换到YCbCr空间,并进行阈值分割和形态学滤波。根据区域的宽高,面积,占空比等形态信息初步过滤。组合感兴趣区域,向水平方向投影,根据波谷位置定位单个交通信号灯区域。将候选区域灰度化,归一化,提取Gabor小波特征,用2维独立分量分析方法降低特征的冗余度,送入最近邻分类器以判断交通信号灯的状态。在3个城市内采集了大量的视频,测试了算法性能,综合性能表明该算法的总体识别率在91%以上,平均处理时间为152ms,能实时,稳定,准确的识别交通信号灯。
构建了交通标志的多目标跟踪模型,定义了交通标志目标的状态。用无迹卡尔曼滤波算法建立单个交通标志目标的状态和运动轨迹模型,预测交通标志目标的位置。而对交通信号灯,则用Kalman滤波跟踪交通信号灯的灯板和灯区域。选择观察序列训练单个和三个交通信号灯目标的隐马尔科夫模型参数,用隐马尔科夫模型算法估计交通信号灯下一时刻的状态信息。
建立了交通标识识别系统的实验平台,设计和实现了交通标识识别的两个子系统：(1)交通标志识别系统；(2)交通信号灯识别系统。详细描述了系统的功能,显示形式,参数设置等。
Since the beginning of the21st century, many researchers and manufacturers invested a lot of human and material resources in the research and development of unmanned vehicle's technology. Road condition perception based on computer vision is one of the important researched contents:recognition on the information of traffic sign and the state of traffic light in both sides of the road. It will provide the decision-making basis for driving the unmanned vehicle.
On the basis of existing research results in the detection, recognition and tracking of the traffic signs at home and abroad, combining the practical application and test performance of the traffic recognition system in the unmanned vehicle, the real-time system of traffic sign recognition and tracking for the unmanned vehicle was designed and constructed. The main research work and achievements are as following:
An imaging process of camera was introduced here. According to the original parameters of CCD(Charge Coupled Device) and intrinsic parameters by the camera calibration, the approximate distance and angle between the traffic sign, and the position of camera on the vehicle are estimated in the road environment. In order to control the exposure time of the camera and adjust the brightness of the image, the brightness information and the exposure value of region which the traffic sign appeared are judged in advance. Different weight matrice are chosen by the exposure of image to calculate the best exposure time. The proposed method can accurately adjust the exposure time of the camera and get image which has appropriate brightness. It is suitable for detection and recognition of the traffic sign.
The detection and recognition algorithm for50symbolic and text traffic signs are presented. RGB value of the image is transformed to highlight the characteristic colors(red, yellow, blue) of the traffic signs. The appropriate thresholds are selected for the image segmentation. The edge of traffic sign's ROI(region of interest) is reconstructed to decrease the error rate. The shapes of traffic sign's ROI are classified and excluded the interference by the signature. For text traffic sign, atrovirens and blue regions are segmented by the thresholds. The regional morphology is judged, and the rectangle region is selected as a traffic sign candidate. The binary image of the candidate region projects to horizontal and vertical direction. The projection curve is fitted by cubic spline interpolation; the curve peak is located to determine the positions of the row and column to separates the text region. Two model representation methods:(1)two dual-tree complex wavelet transform and two-dimensional independent component analysis;(2)template matching based on the internal graphics recognize the traffic sign's type of candidate region respectively. Then the two recognition results are fused and excluded by decision rules. The experimental results show that the recognition rate of traffic sign is more than91%, and the average processing time is171ms. The proposed algorithm has superior performance and is suitable for the sensing system on unmanned vehicle.
Traffic light detection and recognition algorithms for arrow and combination type are proposed. For the arrow traffic light, the position of the board and lamp of traffic lights are located by the color and morphological features of the board and lamp. For the combination of traffic light, the image is transformed by TopHat and convered to YCbCr from RGB space firstly. Then it is segmented by the threshold and filtered by morphology. The regions are preliminarily filtered according to the morphological information such as width, height, area, and duty radio. The ROIs are combined and projected to the horizontal direction. The single region of traffic light is located by the valley. In the traffic light recognition process, the candidate region image is grayscaled and normalized. Feature is extracted by the Gabor wavelet and reduced the redundancy by2dimensional independent component analysis. Feature is sent into the nearest neighbor classifier to judge direction information of the traffic light. Many videoes are collected in3cities and tested algorithm performance. Comprehensive performances show that overall recognition rate of the algorithm is more than91%and the average processing time is152ms. It achieves real-time, stable, accurate goal to recognize traffic light.
The multi-target tracking model of the traffic sign is established. And the target state of the traffic sign is defined. The target state and trajectory model of one traffic sign is established by using unscented Kalman filter to predict the position of traffic sign target. For the traffic light, the board and lamp of the traffic light are tracking by the Kalman filter. Observation sequences are selected to train Hidden Markov Model parameters for one and three traffic light. The next state is estimated by the hidden Markov model algorithm.
The experimental platform of traffic sign recognition system is established. Two subsystems of traffic sign recognition are designed and implemented:(1) the traffic sign recognition system;(2) the traffic light recognition system. It detailedly performs the system function, display form and parameter setting.
引文
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