Vehicle detection in aerial surveillance using dynamic Bayesian networks

We present an automatic vehicle detection system for aerial surveillance in this paper. In this system, we escape from the stereotype and existing frameworks of vehicle detection in aerial surveillance, which are either region based or sliding window based. We design a pixelwise classification method for vehicle detection. The novelty lies in the fact that, in spite of performing pixelwise classification, relations among neighboring pixels in a region are preserved in the feature extraction process. We consider features including vehicle colors and local features. For vehicle color extraction, we utilize a color transform to separate vehicle colors and nonvehicle colors effectively. For edge detection, we apply moment preserving to adjust the thresholds of the Canny edge detector automatically, which increases the adaptability and the accuracy for detection in various aerial images. Afterward, a dynamic Bayesian network (DBN) is constructed for the classification purpose. We convert regional local features into quantitative observations that can be referenced when applying pixelwise classification via DBN. Experiments were conducted on a wide variety of aerial videos. The results demonstrate flexibility and good generalization abilities of the proposed method on a challenging data set with aerial surveillance images taken at different heights and under different camera angles.     

Automatic Moving Vehicle Detection and Classification Based on Artificial Neural Fuzzy Inference System

The main objective of this work is to automatically detect moving vehicles on the road. Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier is adopted in this paper to classify moving vehicles on road. An input traffic video scenes are taken with vertical and horizontal positioned cameras. The proposed system contains six major steps such as preprocessing, vehicle detection, tracking, structural matching, feature extraction and classification. In this proposed method, preprocessing consists of color conversion and noise removal. Vehicle detection is performed by using background subtraction and Otsu thresholding algorithm. Kalman filter is used in the third step to track moving vehicles in successive frames. In the fourth step, Active Shape Modelling method is used to recover the 3D shape of the vehicle in order to find the boundaries of vehicle. In the fifth step, features of the detected vehicles are extracted by Harrish corner detector, log Gabor filter and these features are taken into account to classify the types of vehicle. Finally, ANFIS is proposed to classify the vehicles which is trained by updating the membership function. Experimentation results provides better accuracy rate and low mean error rate when compared with the state of art methods.     

一种快速彩色图像中复杂背景下人脸检测方法

本文提出了一种快速的彩色图像中复杂背景下人脸检测方法.该方法首先利用进化Agent计算对彩色图像中与人的肤色相似的像素进行聚类和区域分割.然后利用小波分解对每一个侯选区域进行人脸特征分析,如所检测到的区域特征分布相似于某一预先定义的人脸模型,则确认该区域代表人脸.实验结果表明,该方法不仅速度快、效率高,而且正确检测率高.     

Binarization of color document images via luminance and saturationcolor features

This paper presents a novel binarization algorithm for color document images. Conventional thresholding methods do not produce satisfactory binarization results for documents with close or mixed foreground colors and background colors. Initially, statistical image features are extracted from the luminance distribution. Then, a decision-tree based binarization method is proposed, which selects various color features to binarize color document images. First, if the document image colors are concentrated within a limited range, saturation is employed. Second, if the image foreground colors are significant, luminance is adopted. Third, if the image background colors are concentrated within a limited range, luminance is also applied. Fourth, if the total number of pixels with low luminance (less than 60) is limited, saturation is applied; else both luminance and saturation are employed. Our experiments include 519 color images, most of which are uniform invoice and name-card document images. The proposed binarization method generates better results than other available methods in shape and connected-component measurements. Also, the binarization method obtains higher recognition accuracy in a commercial OCR system than other comparable methods.     

基于卡尔曼滤波算法的城市交叉路口车辆检测及分类

为了向相关部门提供更多的过往车辆信息以满足道路交通的需求,设计了一个基于卡尔曼滤波算法的城市交叉路口车辆检测及分类系统,用于对过往的车辆进行检测、计数和分类.首先采用背景差分法和卡尔曼滤波算法对在检测区的车辆进行检测和跟踪;然后使用经过检测、处理的被测车辆图像触发距其最近的相机进行图形分割;最后,通过LDA分类器对分段车辆的几何形状及外观特征进行正确地分类.所提系统的有效性在摄取的3400帧视频序列上得到了验证,实验结果表明,系统的检测率可达97.44％,正确分类率可达88.0％,与先进的方法相比,取得了更好的检测性能.     

基于特征融合的背景差分改进算法

背景差分法可完整快速地分割出目标图像,但其在背景扰动与光照变化等情况下检测效果不佳。文中提出一种基于特征融合的背景差分改进算法。该算法将时空局部二值模式纹理特征以及颜色特征相融合,同时考虑两特征的置信度和相似性得分得出背景概率,继而进行前景分割,并将当前检测出的背景像素用于背景模板更新,以便更好地解决复杂背景下的目标检测问题。实验结果表明,新算法的检测效果优于其他同类算法,在保持背景差分算法鲁棒性与复杂度的同时,在背景扰动与光照变化等情况下表现出了良好的检测效果。     

Smoky vehicle detection based on multi-scale block Tamura features

Smoky vehicle, emitting visible black exhaust emissions from vehicle exhaust pipe, is representative heavy pollution vehicle. This paper presents an intelligent smoky vehicle detection method based on multi-scale block Tamura features. In this method, the Vibe background subtraction algorithm is adopted to detect vehicle objects. We propose the multi-scale block Tamura features and use this features to distinguish smoky vehicle images and non-smoke vehicle images. More specifically, the region at the back of the vehicle is divided into 1\(\times \)2 blocks. For each block, the multi-scale strategy based on Gaussian kernel with different standard deviations is proposed to extract features and utilize different scales information. Finally, the back-propagation neural network classifier is trained and used for classification. Our method can automatically detect smoky vehicle through analyzing road surveillance videos. The experimental results show that the proposed algorithm framework performs better than common smoke and fire detection method, and the proposed multi-scale block Tamura features can obtain higher detection accuracy than common Tamura features.     

基于高斯运动模型的车辆检测

针对交通视频监控中常见的双向车道场景,改进得到一种更精确的高斯运动模型,将本不属于同一分布的双向运动车辆对应像素通过变量变换,转换成同一分布。再使用高斯运动模型分别对运动车辆和运动背景建模,通过贝叶斯判定检测出运动车辆。实验结果表明,此方法在单向车道和双向车道的场景中均具有更高的车辆检测准确率,其中在双向车道场景中,检测效果大幅提升。     

Moving Vehicle Detection for Automatic Traffic Monitoring

A video-based traffic monitoring system must be capable of working in various weather and illumination conditions. In this paper, we will propose an example-based algorithm for moving vehicle detection. Different from previous works, this algorithm learns from examples and does not rely on any a priori model for vehicles. First, a novel scheme for adaptive background estimation is introduced. Then, the image is divided into many small nonoverlapped blocks. The candidates of the vehicle part can be found from the blocks if there is some change in gray level between the current image and the background. A low-dimensional feature is produced by applying principal component analysis to two histograms of each candidate, and a classifier based on a support vector machine is designed to classify it as a part of a real vehicle or not. Finally, all classified results are combined, and a parallelogram is built to represent the shape of each vehicle. Experimental results show that our algorithm has a satisfying performance under varied conditions, which can robustly and effectively eliminate the influence of casting shadows, headlights, or bad illumination     

State Machine and Downhill Simplex Approach for Vision‐Based Nighttime Vehicle Detection

In this paper, a novel vision‐based nighttime vehicle detection approach is presented, combining state machines and downhill simplex optimization. In the proposed approach, vehicle detection is modeled as a sequential state transition problem; that is, vehicle arrival, moving, and departure at a chosen detection area. More specifically, the number of bright pixels and their differences, in a chosen area of interest, are calculated and fed into the proposed state machine to detect vehicles. After a vehicle is detected, the location of the headlights is determined using the downhill simplex method. In the proposed optimization process, various headlights were evaluated for possible headlight positions on the detected vehicles; allowing for an optimal headlight position to be located. Simulation results were provided to show the robustness of the proposed approach for nighttime vehicle and headlight detection.     

基于光照不变性模型的运动目标检测新算法

运动目标检测有两大难点,即光照变化的影响,阴影对运动目标准确提取的影响。高志伟等人提出了基于彩色边缘的运动车辆检测,实现了复杂背景下的运动目标检测,但该方法无法消除阴影的影响。为了克服光照及阴影的影响,提出了基于一维不变性图像的背景模型,更新了最小熵投影角度,设计了运动目标检测的新算法。以交通运输领域为例,将本文算法和多层前景算法、边缘检测算法做了对比,通过实验验证了该方法在检测运动目标时能够克服光照变化影响,并有效抑制阴影。     

Vehicle detection and inter-vehicle distance estimation using single-lens video camera on urban/suburb roads

This paper presents a driver assistance system for vehicle detection and inter-vehicle distance estimation using a single-lens video camera on urban/suburb roads. The task of vehicle detection on urban/suburb roads is more challenging due to their high scene complexity. In this work, the still area of frame inside the host vehicle is first removed using temporal differencing, followed by detecting vanishing point. Segmentation of road regions is then conducted using vanishing point and road’s edge lines. Shadow regions at the bottoms of vehicles verified using the HOG feature and an SVM classifier are utilized to detect vehicle positions. The distances between the host and its front vehicles are estimated based on the locations of detected vehicles and vanishing point. Experimental results show varied performance of vehicle detection with different scenes of urban/suburb roads and the detection rate can achieve up to 94.08%, indicating the feasibility of the proposed method.     

基于改进GMM和多特征融合的视频火焰检测算法

针对现有视频图像火焰检测算法前景提取不完整、准确率低和误检率高等问题,提出一种基于改进混合高斯模型(GMM)和多特征融合的视频火焰检测算法。首先针对背景建模,提出了自适应高斯分布数和学习率的改进GMM方法,以提高前景提取效果和算法实时性;然后利用火焰颜色特征筛选出疑似火焰区域,再通过融合改进局部二值模式纹理和边缘相似度特征用于火焰检测。基于支持向量机设计火焰融合特征分类器并进行对比实验,在公开数据集上的实验结果表明,所提算法有效提高了背景建模效果,火焰检测准确率可达到92.26%,误检率低至2.43%。     

基于多阈值和改进的Hough变换检测电表接线圆孔尺寸

针对智能电表接线圆孔尺寸自动检测的问题,提 出一种以多阈值分割方法为主体并基于改进的随机Hough变换的 圆检测方法。首先,实时获得包含所有智能电表接线圆孔并经裁剪的彩色图像,经图像灰度 化、滤波处理后,通过阈值分割 得到能够反应圆孔特征的二值图像。基于连通域标记算法准确将包含多个接线圆孔的图像自 动裁剪为至多包含一个真实接线 圆孔的多个子图像。其次,对于每个子图像,应用给定范围内的连续阈值进行Canny边界提 取,并通过改进的随机Hough变 换提取圆信息,进行圆累积。最后,根据峰值大小和位置提取有效圆及半径。对大量智能电 表接线圆孔图像进行实验的结果 表明,该方法能准确地提取出外观颜色、圆孔尺寸各异的接线圆孔,圆心坐标平均误差不超 过2个像素,圆半径平均误差不 超过2.5mm,是实时电表接线圆孔信息表征的新尝试。     

高性能交通标志检测模块的VLSI结构设计

交通标志检测是驾驶辅助系统的重要功能，但对实时性极高的要求使其非常具有挑战性。提出了一种高性能禁令标志检测模块的VLSI结构，并在FPGA平台上完成了实现和验证。该结构的基本原理是同时利用颜色与形状特征，在图像的红色边缘位图中采用圆霍夫变换检测圆形。通过挖掘圆霍夫变换的局部特性，所提出的结构在内存占用方面显著低于常规结构。所有半径同时投票的设计使FPGA的逻辑单元和内存的并行性得以充分发挥。该结构在Altera公司的EP3C55F484C6型FPGA上进行了验证，其最大可运行频率达到122 MHz，且资源占用在可接受范围内。实验结果表明:该结构的吞吐量达到115 M像素/s，且对低光照条件、局部遮挡、多标志相连和相似背景颜色等不利条件具有良好的适应能力。     

复杂行车环境下的前方车辆检测算法研究

针对复杂行车环境下的智能车辆行车安全问题,提出了一种基于特征的多目标前方车辆检测算法。算法首先利用车辆底部阴影特征、车辆轮廓特征的先验知识,探测前方感兴趣区域,然后利用车辆边界特征、对称性特征对感兴趣区域进行判别。该算法针对复杂路况下的车辆分布特征进行了适应性设计。能够快速、准确的检测到具有潜在安全威胁的前方行驶车辆。     

一种改进的边缘生长彩色图像分割方法

针对彩色图像颜色空间特性,提出一种改进的边缘生长图像分割方法。首先根据平均颜色矩确定图像量化级数,色彩量化后采用边缘检测提取边缘像素集,并将这些高细节点形成边缘线,围成一个封闭的区域,最后根据颜色空间的区域距离,将初始分割区域进行合并。该方法很好的解决了使用边缘检测或区域生长所产生的不连续性和过分割问题。实验结果显示对彩色图像分割具有较好的效果。     

基于分形特征融合的目标边缘检测算法

针对复杂背景下目标边缘检测问题,研究了分形在目标检测中的应用,提出了基于分形特征融合的目标边缘检测算法。首先求取图像的分形维数和几何度量空间变换率,然后运用Dempster-Shafer证据理论对分形维数和几何度量空间变换率进行融合处理,对图像像素分类,从而得到目标边缘,最后细化边缘得到目标检测结果。实验表明,该算法能够克服复杂背景的干扰,有效地提取目标边缘。     

Localizing text in scene images by boundary clustering, stroke segmentation, and string fragment classification

In this paper, we propose a novel framework to extract text regions from scene images with complex backgrounds and multiple text appearances. This framework consists of three main steps: boundary clustering (BC), stroke segmentation, and string fragment classification. In BC, we propose a new bigram-color-uniformity-based method to model both text and attachment surface, and cluster edge pixels based on color pairs and spatial positions into boundary layers. Then, stroke segmentation is performed at each boundary layer by color assignment to extract character candidates. We propose two algorithms to combine the structural analysis of text stroke with color assignment and filter out background interferences. Further, we design a robust string fragment classification based on Gabor-based text features. The features are obtained from feature maps of gradient, stroke distribution, and stroke width. The proposed framework of text localization is evaluated on scene images, born-digital images, broadcast video images, and images of handheld objects captured by blind persons. Experimental results on respective datasets demonstrate that the framework outperforms state-of-the-art localization algorithms.     

Motion detection using the randomised Hough transform: exploitinggradient information and detecting multiple moving objects

An algorithm to detect one moving object using the randomised Hough transform (RHT) has previously been proposed. This new nonmodel-based method, called motion detection using randomised Hough transform (MDRHT), was shown be to applicable to translational and rotational motion detection of one moving object. The basic, earlier version of the MDRHT utilises edge points only as its features. The MDRHT is extended to use both edge pixels and the intensity-gradient vector at edge pixels. Moreover, the MDRHT method is generalised to detect also multiple moving objects. The translational motion experiments with the variant of the technique using gradient information and coping with several moving objects give promising results in two-dimensional-motion detection and estimation, compared with the earlier version of the MDRHT