An efficient continuous tracking system in real-time surveillance application

A tracking object must present a proper field of view (FOV) in a multiple active camera surveillance system; its clarity can facilitate smooth processing by the surveillance system before further processing, such as face recognition. However, when pan–tilt–zoom (PTZ) cameras are used, the tracking object can be brought into the FOV by adjusting its intrinsic parameters; consequently, selection of the best-performing camera is critical. Performance is determined by the relative positions of the camera and the tracking objects, image quality, lighting and how much of the front side of the object faces the camera. In a multi-camera surveillance system, both camera hand-off and camera assignment play an important role in automated and persistent tracking, which are typical surveillance requirements. This study investigates the use of automatic methods for tracking an object across cameras in a surveillance network using PTZ cameras. An automatic, efficient continuous tracking scheme is developed. The goal is to determine the decision criteria for hand-off using Sight Quality Indication (SQI) (which includes information on the position of the object and the proportion of the front of object faces the camera), and to perform the camera handoff task in a manner that optimizes the vision effect associated with monitoring. Experimental results reveal that the proposed algorithm can be efficiently executed, and the handoff method for feasible and continuously tracking active objects under real-time surveillance.     

基于Adept机器人的视觉伺服控制系统

机器人视觉伺服控制在理论和应用等方面还有许多问题需要研究,例如特征选择、系统标定和伺服控制算法等.针对Adept机器人,提出了一种简单快速的不需要精确标定摄像机内外部参数的摄像机标定方法,完成了从被观测物体表面所在的视觉平面坐标系到机器人基坐标系的坐标变换.使用图像的全局特征,即图像矩特征进行伺服跟踪;利用所推导的图像雅可比矩阵,设计了由图像反馈与目标运动自适应补偿组成的视觉伺服控制器.将算法对静态目标的定位实验进行了验证,然后又将其应用到移动目标的跟踪上,通过调节和优选控制参数,实现了稳定的伺服跟踪和抓取.实验结果表明采用图像矩作为图像特征能够避免复杂的特征匹配过程,并且能够获得较好的跟踪精度.     

基于AlexNet算法的道路能见度估测方法

本文采用AlexNet神经网络算法构建一个高速公路能见度识别的框架，通过对道路摄像头图像的采集，对图像进行标注、对AlexNet算法进行训练，提取图像能见度特征，构建能见度等级识别模型，实时接入道路摄像头图像，实现能见度值的估测。通过对安徽省高速公路42个监控摄像机进行图像的采集，抽取标注有能见度值的15万余幅样本，进行能见度识别结果分析，结果显示平均识别率达到78.02%，其中有14个站点的识别率超过90%，21个站点的识别率在80%以上。基于AlexNet算法的道路能见度估测方法能够满足道路能见度实时性和识别准确率的要求，可以作为能见度仪未安装地区的能见度辅助监测方法，同时对于光照变化、远近距离等都具有良好的鲁棒性。     

Robust real-time ground plane motion compensation from a moving vehicle

One method to detect obstacles from a vehicle moving on a planar road surface is the analysis of motion-compensated difference images. In this contribution, a motion compensation algorithm is presented, which computes the required image-warping parameters from an estimate of the relative motion between camera and ground plane. The proposed algorithm estimates the warping parameters from displacements at image corners and image edges. It exploits the estimated confidence of the displacements to cope robustly with outliers. Knowledge about camera calibration, measuremts from odometry, and the previous estimate are used for motion prediction and to stabilize the estimation process when there is not enough information available in the measured image displacements. The motion compensation algorithm has been integrated with modules for obstacle detection and lane tracking. This system has been integrated in experimental vehicles and runs in real time with an overall cycle of 12.5 Hz on low-cost standard hardware. Received: 23 April 1998 / Accepted: 25 August 1999     

公路汽车视觉导航中运动目标的检测与跟踪方法的研究

通过对室外行驶车辆上所采集的长序列图象的处理与分析,研究公路汽车计算机辅助礼堂导航中运动目标的检测与跟踪问题。算法的核心是根据图象中的水平、垂直边缘及目标尾部的局部对称性检测和图象中目标的存在,进而建立描述目标与摄象机之间的相对运动的状态模型和表示目标循征的透视投影模型,应用扩展的卡尔曼滤波器进行3维运动参数估计。     

Vision-Based Road-Following Using Proportional Navigation

This paper describes a new approach for autonomous road following for an unmanned air vehicle (UAV) using a visual sensor. A road is defined as any continuous, extended, curvilinear feature, which can include city streets, highways, and dirt roads, as well as forest-fire perimeters, shorelines, and fenced borders. To achieve autonomous road-following, this paper utilizes Proportional Navigation as the basis for the guidance law, where visual information is directly fed back into the controller. The tracking target for the Proportional Navigation algorithm is chosen as the position on the edge of the camera frame at which the road flows into the image. Therefore, each frame in the video stream only needs to be searched on the edge of the frame, thereby significantly reducing the computational requirements of the computer vision algorithms. The tracking error defined in the camera reference frame shows that the Proportional Navigation guidance law results in a steady-state error caused by bends and turns in the road, which are perceived as road motion. The guidance algorithm is therefore adjusted using Augmented Proportional Navigation Guidance to account for the perceived road accelerations and to force the steady-state error to zero. The effectiveness of the solution is demonstrated through high-fidelity simulations, and with flight tests using a small autonomous UAV.     

Tracking human motion in structured environments using adistributed-camera system

This paper presents a comprehensive framework for tracking coarse human models from sequences of synchronized monocular grayscale images in multiple camera coordinates. It demonstrates the feasibility of an end-to-end person tracking system using a unique combination of motion analysis on 3D geometry in different camera coordinates and other existing techniques in motion detection, segmentation, and pattern recognition. The system starts with tracking from a single camera view. When the system predicts that the active camera will no longer have a good view of the subject of interest, tracking will be switched to another camera which provides a better view and requires the least switching to continue tracking. The nonrigidity of the human body is addressed by matching points of the middle line of the human image, spatially and temporally, using Bayesian classification schemes. Multivariate normal distributions are employed to model class-conditional densities of the features for tracking, such as location, intensity, and geometric features. Limited degrees of occlusion are tolerated within the system. Experimental results using a prototype system are presented and the performance of the algorithm is evaluated to demonstrate its feasibility for real time applications     

Real-time view-based pose recognition and interpolation for tracking initialization

In this paper we propose a new approach to real-time view-based pose recognition and interpolation. Pose recognition is particularly useful for identifying camera views in databases, video sequences, video streams, and live recordings. All of these applications require a fast pose recognition process, in many cases video real-time. It should further be possible to extend the database with new material, i.e., to update the recognition system online. The method that we propose is based on P-channels, a special kind of information representation which combines advantages of histograms and local linear models. Our approach is motivated by its similarity to information representation in biological systems but its main advantage is its robustness against common distortions such as clutter and occlusion. The recognition algorithm consists of three steps: (1) low-level image features for color and local orientation are extracted in each point of the image; (2) these features are encoded into P-channels by combining similar features within local image regions; (3) the query P-channels are compared to a set of prototype P-channels in a database using a least-squares approach. The algorithm is applied in two scene registration experiments with fisheye camera data, one for pose interpolation from synthetic images and one for finding the nearest view in a set of real images. The method compares favorable to SIFT-based methods, in particular concerning interpolation. The method can be used for initializing pose-tracking systems, either when starting the tracking or when the tracking has failed and the system needs to re-initialize. Due to its real-time performance, the method can also be embedded directly into the tracking system, allowing a sensor fusion unit choosing dynamically between the frame-by-frame tracking and the pose recognition.     

Real-time multiple vehicle detection and tracking from a moving vehicle

Abstract. A real-time vision system has been developed that analyzes color videos taken from a forward-looking video camera in a car driving on a highway. The system uses a combination of color, edge, and motion information to recognize and track the road boundaries, lane markings and other vehicles on the road. Cars are recognized by matching templates that are cropped from the input data online and by detecting highway scene features and evaluating how they relate to each other. Cars are also detected by temporal differencing and by tracking motion parameters that are typical for cars. The system recognizes and tracks road boundaries and lane markings using a recursive least-squares filter. Experimental results demonstrate robust, real-time car detection and tracking over thousands of image frames. The data includes video taken under difficult visibility conditions. Received: 1 September 1998 / Accepted: 22 February 2000     

大场景中物体运动轨迹的测量

提出一种基于旋转摄像机拍摄的物体运动轨迹测量方法。该方法采用旋转摄像机跟踪拍摄运动物体,通过摄像机标定技术建立图像平面与物体运动平面的对应关系,完成对物体运动轨迹的测量。该测量方法解决了单台摄像机拍摄范围和拍摄精度的约束,与多摄像机协同拍摄相比,需要的测量设备少,容易操作,对场地的适应能力强。     

OpenMV视觉模块的滚球目标识别与追踪系统研究

基于OpenMV微型机器视觉模块,以云台为载体,使用Micro Python语言对滚球目标识别与追踪算法进行研究。通过摄像头获取实时图像,在对图形进行预处理后利用形状识别和边缘检测算法完成目标识别与追踪,并通过串口将滚球中心位置坐标输出。     

基于运动模型的道路识别与跟踪算法的研究

自主驾驶与辅助导航是目前国际上研究的热问题,通过对室外行驶车辆上的ＣＣＤ摄像机所采集的长序列立体图象的处理与分析,研究公路汽车自动视觉导航中的道路识别与跟踪问题。     

Constrained pose refinement of parametric objects

Pose refinement is an essential task for computer vision systems that require the calibration and verification of model and camera parameters. Typical domains include the real-time tracking of objects and verification in model-based recognition systems. A technique is presented for recovering model and camera parameters of 3D objects from a single two-dimensional image. This basic problem is further complicated by the incorporation of simple bounds on the model and camera parameters and linear constraints restricting some subset of object parameters to a specific relationship. It is demonstrated in this paper that this constrained pose refinement formulation is no more difficult than the original problem based on numerical analysis techniques, including active set methods and lagrange multiplier analysis. A number of bounded and linearly constrained parametric models are tested and convergence to proper values occurs from a wide range of initial error, utilizing minimal matching information (relative to the number of parameters and components). The ability to recover model parameters in a constrained search space will thus simplify associated object recognition problems.     

多发光目标的高速跟踪

研究了一种基于高速图像采集与处理的多发光目标跟踪技术.采用现场可编程门阵列(FPGA)形成高速Camera Link CMOS相机的图像采集与实时处理系统.通过对图像数据进行开窗、求重心等算法处理,实现对多发光目标的跟踪,并搭建由直流电机驱动、具有两个LED光源的发光靶标进行跟踪测试.测试结果表明:相机输出帧频100 Hz时,运动目标相对于相机角速度达1.93 rad/s,仍可实时跟踪.系统的跟踪能力正比于相机帧频.     

Real-time vehicle tracking mechanism with license plate recognition from road images

Abstract Information about vehicles on the road is very important for the maintenance of traffic control in current complex traffic condition. Images of vehicles are captured by vehicle-directed cameras. This paper proposes a new vehicle tracking mechanism using license plate recognition technology, which is essential to having information about vehicles on the roads. The proposed method is a real-time processing system using multistep image processing, as well as recognition and tracking processes from 2D and 3D images. The experimental results of real environmental images in recognition and tracking using the proposed method are shown.     

基于计算机视觉的虚实场景合成方法研究

开发了一种虚拟场景与实时视频之间的合成技术,提出了一种新的基于非定标技术的虚实配准方法,有效地解决了基于标识的三雏注册系统要求摄像机内参在系统运行过程中不能发生改变的限制.详细介绍了系统所采用的基于计算机视觉的标识识别和实时自动摄像机位置、姿态跟踪算法,并给出了系统运行结果,成功地将该技术应用于虚拟规划系统中.     

Fast, reliable head tracking under varying illumination: anapproach based on registration of texture-mapped 3D models

A technique for 3D head tracking under varying illumination is proposed. The head is modeled as a texture mapped cylinder. Tracking is formulated as an image registration problem in the cylinder's texture map image. The resulting dynamic texture map provides a stabilized view of the face that can be used as input to many existing 2D techniques for face recognition, facial expressions analysis, lip reading, and eye tracking. To solve the registration problem with lighting variation and head motion, the residual registration error is modeled as a linear combination of texture warping templates and orthogonal illumination templates. Fast stable online tracking is achieved via regularized weighted least-squares error minimization. The regularization tends to limit potential ambiguities that arise in the warping and illumination templates. It enables stable tracking over extended sequences. Tracking does not require a precise initial model fit; the system is initialized automatically using a simple 2D face detector. It is assumed that the target is facing the camera in the first frame. The formulation uses texture mapping hardware. The nonoptimized implementation runs at about 15 frames per second on a SGI O2 graphic workstation. Extensive experiments evaluating the effectiveness of the formulation are reported. The sensitivity of the technique to illumination, regularization parameters, errors in the initial positioning, and internal camera parameters are analyzed. Examples and applications of tracking are reported     

基于深度学习的多旋翼无人机单目视觉目标定位追踪方法

针对无人机对目标的识别定位与跟踪,本文提出了一种基于深度学习的多旋翼无人机单目视觉目标识别跟踪方法,解决了传统的基于双目摄像机成本过高以及在复杂环境下识别准确率较低的问题。该方法基于深度学习卷积神经网络的目标检测算法,使用该算法对目标进行模型训练,将训练好的模型加载到搭载ROS的机载电脑。机载电脑外接单目摄像机,单目摄像头检测目标后,自动检测出目标在图像中的位置,通过采用一种基于坐标求差的优化算法进行目标位置准确获取,然后将目标位置信息转化为控制无人机飞行的期望速度和高度发送给飞控板,飞控板接收到机载电脑发送的跟踪指令,实现对目标物体的跟踪。试验结果验证了该方法可以很好的进行目标识别并实现目标追踪     

A bio-inspired tracking camera system

We propose a bio-inspired reconfigurable tracking camera system using FPGAs. In the system, a wide-lens camera captures an entire image, while a zoom-lens camera tracks a target in the image and magnifies it. In the current system, a probabilistic neural network (PNN) and mosaic processing are used for the image recognition and preprocessing, respectively. Thanks to the FPGA-based design, not only the PNN and the mosaic processing, but also other recognition and preprocessing algorithms can be implemented onto the system to adapt various images and targets. This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006     

基于侧影轮廓的图像三维重建方法

针对自标定单幅图像的三维重建问题,提出一种基于侧影轮廓的图像三维重建方法。使用成角度平面镜装置拍摄目标物体,对所得图像进行边缘跟踪,得到闭合的轮廓曲线,利用满足平面镜成像原理的2对侧影轮廓计算灭点,根据灭点间的约束关系计算相机参数,在此基础上重建得到物体的三维模型。实验结果表明,该方法能快速重建逼真的三维模型。