于混合高斯莫型与联合特征的行人检测方法

针对视频环境下行人检测多数采用窗口滑动方法识别慢、不能快速找到行人窗口的缺点,提出了一种基于组合算法的行人目标识别方法,利用高斯混合模型方法提取视频中的运动前景,划定一个泛目标窗口,再使用HOG-l bp联合特征训练的分类器对泛目标窗口进行分类,得到分类结果,对行人目标进行标记.经实验验证:该方法相对于当前行人检测方法,检测速度和正确率都取得了很好的效果.     

结合运动信息与表观特征的行人检测方法

提出一种结合运动信息与表观特征的行人检测方法.在对通过表观检测子获得的候选检测窗口执行分割验证的框架中,将运动信息融入到基于图像序列的对象分割算法中,通过获取更准确的分割结果来提高对候选检测窗口的检测准确率.该方法利用运动信息更新运动对象的前景/背景分布模型,将颜色信息间接地融入行人检测中,并通过形状特征表现出来,与行人表观检测子形成互补的特性,获得更好的检测结果.上述结论在CAVIAR视频以及行人检测视频中得到了实验验证.     

基于凸包裁剪的行人视频检测算法

为了解决行人群体视频检测的难题,提出一种基于凸包裁剪的行人视频检测算法。该算法采用局部凸包技术搜索行人外部轮廓,利用凹点挖掘技术裁剪轮廓曲线,建立相应规则排除非头部线段,通过最小二乘拟合法对头部曲线进行快速椭圆检测。实验结果表明,该算法能准确地检测出重叠或连通的头部,排除非头部物体,且处理速度快,实际应用价值高。     

利用腿部区域HOG特征的行人检测方法

为快速定位车辆前方的行人,提出一种基于腿部感兴趣区域梯度方向直方图(HOG)特征的行人检测方法。将可能存在行人腿部的区域作为感兴趣区域,采用Sobel算子增强腿部垂直边缘特征,并提取梯度方向直方图特征,有效地降低了特征向量的维数;在检测过程中仅扫描可能存在行人腿部的图像下半部分,并在整幅图像的块内计算HOG特征,减少了复杂背景对行人检测干扰,进一步简化了检测过程;基于垂直边缘对称性特征对检测结果进行融合。实验结果表明,该算法能在保持检测率的同时提高检测速度。     

激光雷达引导下的红外图像中行人检测

研究了一种利用激光雷达数据引导红外图像进行行人检测与识别的方法。首先针对激光雷达数据,提出了一种利用鲁棒主成分分析进行目标感兴趣区域检测的方法,进而设计了一种窗口滤波算法对前景矩阵进行滤波处理,得到目标感兴趣区域的位置信息。在此基础上,将该位置信息投影到红外图像中获取红外图像中的目标感兴趣区域,进而在红外图像感兴趣区域内利用稀疏编码金字塔算法和支持向量机完成行人识别。实验结果表明了该算法能够有效地完成行人识别。     

基于随机森林的层次行人检测算法

针对视频和图像中快速、准确的行人检测问题,提出了一种分层次的、全局信息和局部信息相结合的行人检测算法.该方法以随机森林分类器为基础,利用图像金字塔模型融合行人的多层信息.首先,在低尺度空间利用主方向模板(DOT)特征和随机森林算法训练行人的全局分类器,第一层检测在低尺度空间中进行,找到行人的候选区域;然后,在高尺度空间提取图像块集合,基于部件随机森林训练行人的局部外观和几何约束模型;最后,基于上层的候选区域,在高尺度空间利用霍夫投票进行第二层精确检测.实验结果表明,该方法有更低的时间复杂度,并提升了行人检测的准确率,全局信息和局部信息的层次融合,能有效解决快速、准确的行人检测问题.     

基于肤色检测的AdaBoost人脸检测算法改进

AdaBoost人脸检测算法用于嵌入式实时高清视频时检测速度缓慢.为此,提出一种改进的人脸检测算法.对图像做肤色检测,将检测到的区域进行形态学处理,并作为感兴趣区域,完成AdaBoost人脸检测,以得到检测结果.实验结果表明,该算法在嵌入式系统上运行稳定,能提高检测速度和检测正确率.     

面向视频监控的自动行人检测

为了解决目前行人检测技术的检测速度和准确性之间的平衡问题,对基于视频的行人检测技术进行了研究,提出了利用LUV颜色空间信息与C4行人检测算法相结合的视频自动行人检测方法(LUVC4)。首先利用C4行人检测算法快速遍历视频的每帧图像,当得到的窗口置信度在可疑区间时,再进一步对该窗口做LUV颜色空间检测。如果两次检测的加权和分数满足阈值,则判别为行人。通过大量实验表明,该方法在检测速度几乎能达到C4速度的同时,还能在FPPI为0.1时降低约9%的漏检率。     

基于矩形拟合的人体检测

提出了一种在单幅图像中检测人体的新算法.将人体看作由躯干和四肢构成.用一个四边形拟合躯干,用一个或者两个四边形拟合每个肢体.人体检测就是寻找满足一定几何和拓扑约束的四边形组合.首先检测与拟合图像中的矩形区域,然后搜索满足几何和拓扑约束的矩形组合.如果该矩形组合的概率大于阈值,则调整各个矩形顶点的坐标,以便得到一个紧凑的人体模型表示.最后,重新计算该紧凑模型的概率.实验结果表明,该方法可以用于基于内容的图像检索以及基于视频的人体运动分析.     

基于ROI区域强分辨力HOG特征的视频行人检测

针对传统梯度方向直方图(HOG)行人检测系统中检测窗扫描区域过大、HOG特征维度大而引起的检测速度慢问题,提出了改进的视频行人检测算法.通过运动信息提取感兴趣(ROI)目标区域,利用Fisher准则和多尺度特性选取具有强分辨力的行人HOG特征从而降低特征维数,结合支持向量机(SVM)检测行人.实验结果表明,本文方法在保证视频行人检测的准确率的同时,有效地提高了行人检测的速率.     

一种基于视觉特征区域建议的目标检测方法

虽然基于深度学习的目标检测器具有较高的检测精度,但是大多数检测器的检测速度不能满足实时性要求.此外,目前主流的实时检测算法如SSD(single shot multibox detector)和YOLO(you only look once),对小目标的检测精度不高.鉴于此,提出一种基于视觉特征区域建议的目标检测算法,能够综合平衡检测精度和检测速度.算法分为区域建议和网络分类,区域建议根据目标的特征信息提取候选区域ROI(region of interest);网络分类使用CNN(convolutional neural network)对区域建议中提取的ROI进行处理,计算每个ROI类别的置信度,置信度大于设定阈值的ROI即为目标检测结果.实验结果表明,所提出算法的检测精度明显高于Faster R-CNN、SSD和YOLO,并且具有接近SSD和YOLO的检测速度.     

基于ARM9的整精米率检测仪设计与实现

为改善目前整精米率检测方法落后的现状，提出了一种基于ARM9的整精米率检测系统的软件设计方案及实现方法。该检测系统基于图像传感技术，用于自动检测、计算并显示稻谷整精米率并生成整精米率检测报告。中控系统采用Micro2440开发板，基于Qtopia软件开发平台调用嵌入式数据库Sqlite3，完成了人机交互界面的设计与实现，并且与系统其他硬件设备相结合通过串口、网口通信控制硬件各部分协调工作，实现了整体仪器的运行。测试结果表明，系统实用性强、效率高且测试结果较为准确。     

基于感兴趣区梯度方向直方图的行人检测

针对以梯度方向直方图作为人体特征的行人检测存在向量维数较大、检测时间较长的问题,提出基于感兴趣区梯度方向直方图的行人检测方法,分别在头部及四肢等重点区域计算梯度方向直方图,有效地减少了向量维数。实验结果表明,该方法在检测率基本不变的情况下提高了检测速度。     

Integrated features and GMM Based Hand Detector Applied to Character Recognition System under Practical Conditions

Detection of bare-hand under non-ideal conditions is a challenging task. Most of the existing hand detection systems are developed under limited environmental constraints. In this study, a robust two-level bare-hand detector is integrated with a 58 keyboard characters recognition model. At first, the Gaussian mixture model (GMM) based foreground detector is used to segment the region of interest (ROI), which is further classified using Color-texture and texture based models to detect the actual fist. The detected hand is tracked using modified Kanade–Lucas–Tomasi (KLT) tracker to generate the required trajectory points of the character. The feature space for character recognition consists of existing features and three new features, namely, Local Geometrical Area Ratio (LGAR), Area of two halves (ATH), Curve-Area feature (CAF) that are extracted from the trajectory points. Feature space is optimized using statistical analysis algorithms. Multi-factor analysis of individual character subsets such as alphabets, numbers, ASCII characters, etc., are carried out using multiple conventional classifiers along with Support vector machine (SVM), extreme learning machine (ELM), artificial neural network (ANN), and proposed Neuro-fuzzy classifiers. The proposed GMM based motion detection method achieves an accuracy of 100% during the segmentation of ROI, followed by an increase of 46.77% in the accuracy of two-level hand detection under non-ideal conditions. Maximum accuracy of 58 character system using proposed features and ANN classifier is observed to be 92.56%.

     

Semantic fusion of laser and vision in pedestrian detection

Fusion of laser and vision in object detection has been accomplished by two main approaches: (1) independent integration of sensor-driven features or sensor-driven classifiers, or (2) a region of interest (ROI) is found by laser segmentation and an image classifier is used to name the projected ROI. Here, we propose a novel fusion approach based on semantic information, and embodied on many levels. Sensor fusion is based on spatial relationship of parts-based classifiers, being performed via a Markov logic network. The proposed system deals with partial segments, it is able to recover depth information even if the laser fails, and the integration is modeled through contextual information—characteristics not found on previous approaches. Experiments in pedestrian detection demonstrate the effectiveness of our method over data sets gathered in urban scenarios.     

基于车内外视觉信息的行人碰撞预警方法

行人碰撞预警系统通常依据行人检测与碰撞时间判断的方式为驾驶员提供预警信息。为了提供更加可靠的危险判断依据，本文提出一种同时分析道路状况与驾驶员头部姿态的行人碰撞预警方法，用两个单目相机分别获取车辆内外环境图像。通道特征检测器用于定位行人，根据单目视觉距离测量方法估计出行人与自车间的纵向与横向距离。多任务级联卷积网络用于定位驾驶员面部特征点，通过求解多点透视问题获取头部方向角以反映驾驶员注意状态。结合行人位置信息与驾驶员状态信息，本文构建模糊推理系统判断碰撞风险等级。在实际路况下的实验结果表明，根据模糊系统输出的风险等级可以为预防碰撞提供有效的指导。     

基于兴趣域检测的空间金字塔匹配图像分类

在图像分类过程中,一个图像区域内起着决定性作用的对象位置和大小都不确定,直接使用空间金字塔匹配算法的分类准确率并不高。为此,提出了一种基于兴趣域检测的空间金字塔匹配方法可以有效改善分类准确率。首先利用检测器得到的定位结果,证实了在图像分类里使用一种主流的目标检测算法去将图像的目标和背景分离,分别得到前景和背景的可行性,然后使用粗目标对齐方式匹配,为这两个区域分别构建基于空间金字塔匹配算法的空间特征直方图,最后结合检测器提供的兴趣域检测评分与支持向量机提供的评分为分类结果重评分。实验结果表明,所提方法比使用标准的空间金字塔匹配算法得到的平均准确度均值提升超过12%,同时在与三种主流算法的对比中,所提方法平均准确度均值最高,并且在超过一半的图像类别中获得了最高的平均准确度。     

Content-Based Image Retrieval Based on ROI Detection and Relevance Feedback

Content-based image retrieval is an important research topic in computer vision. We present a new method that combines region of interest (ROI) detection and relevance feedback. The ROI based approach is more accurate in describing the image content than using global features, and the relevance feedback makes the system to be adaptive to subjective human perception. The feedback information is utilized to discover the subjective ROI perception of a particular user, and it is further employed to recompute the features associated with ROIs with the updated personalized ROI preference. A fast computation technique is proposed to avoid repeating the ROI detection for images in the database. It directly estimates the features of the ROIs, which makes the query process fast and efficient. For illustration of the overall approach, we use the color saliency and wavelet feature saliency to determine the ROIs. Normalized projections are selected to represent the shape features associated with the ROIs. Experimental results show that the proposed system has better performance than the global features based approaches and region based techniques without feedback.     

检测器和分类器级联的飞机发动机损伤检测

目的 基于深度学习的飞机发动机损伤检测是计算机视觉中的一个新问题。当前的目标检测方法没有考虑飞机发动机损伤检测问题的特殊性,将其直接用于发动机损伤检测的效果较差,无法满足实际使用的要求。为了提高损伤检测的精度,提出检测器和分类器级联的发动机损伤检测方法：Cascade-YOLO （cascade-you only look once）。方法 首先,将损伤区域作为正例、正常区域作为负例,训练损伤检测网络,初始化特征提取网络的网络参数;其次,固定特征提取网络,使用多个检测头分别检测不同类型的发动机损伤,每个检测头独立进行检测,从而提高单类别损伤的检测召回率;最后,对于置信度在一定范围内的损伤,训练一个多分类判别器,用于校正检测头输出的损伤类别。基于检测结果,利用语义分割分支可以准确分割出损伤区域。结果 构建了一个具有1 305幅且包含9种损伤类型的孔探图像数据集,并在该数据集上量化、对比了6个先进的目标检测方法。本文方法的平均精确率（mean average precision,MAP）、准确率、召回率相比单阶段检测器YOLO v5分别提高了2.49%、12.59%和12.46%。结论 本文提出的检测器和分类器级联的发动机损伤检测模型通过对每类缺陷针对性地训练单独的检测头,充分考虑了不同缺陷间的分布差异,在提高召回率的同时提升了检测精度。同时该模型易于扩展类别,并可以快速应用于分割任务,符合实际的应用需求。     

Robust people detection using depth information from an overhead Time-of-Flight camera

In this paper we describe a system for the automatic detection of multiple people in a scene, by only using depth information provided by a Time of Flight (ToF) camera placed in overhead position. The main contribution of this work lies in the proposal of a methodology for determining the Regions of Interest (ROI’s) and feature extraction, which result in a robust discrimination between people with or without accessories and objects (either static or dynamic), even when people and objects are close together. Since only depth information is used, the developed system guarantees users’ privacy. The designed algorithm includes two stages: an online stage, and an offline one. In the offline stage, a new depth image dataset has been recorded and labeled, and the labeled images have been used to train a classifier. The online stage is based on robustly detecting local maximums in the depth image (which are candidates to correspond to the head of the people present in the scene), from which a carefully ROI is defined around each of them. For each ROI, a feature vector is extracted, providing information on the top view of people and objects, including information related to the expected overhead morphology of the head and shoulders. The online stage also includes a pre-filtering process, in order to reduce noise in the depth images. Finally, there is a classification process based on Principal Components Analysis (PCA). The online stage works in real time at an average of 150 fps. In order to evaluate the proposal, a wide experimental validation has been carried out, including different number of people simultaneously present in the scene, as well as people with different heights, complexions, and accessories. The obtained results are very satisfactory, with a 3.1% average error rate.