Visual vehicle tracking algorithm

A modified extended Kalman filter (EKF) is proposed for visual vehicle tracking by adding a new objective function to reduce the sensitivity of the filter to model uncertainty. Experimental results show that the performance of this approach is much better than the traditional EKF and its recent variant     

增强现实中视觉图像抗遮挡运动目标跟踪算法研究

为了减少视觉图像在处理运动目标跟踪时所需的消耗时间,设计一种基于增强现实技术的视觉图像抗遮挡运动目标跟踪算法.利用噪声去除结果标记图像中的目标连通区域,进而提取特征值数据指标,完成视觉图像的前景处理.在此基础上,设计集成分类器结构,根据跟踪评价指标的选取结果建立跟踪算法流程,完成增强现实中视觉图像抗遮挡运动目标的跟踪算...     

一种自动改变窗口尺寸的跟踪算法

在动态目标跟踪过程中,实用的跟踪系统要求能实时地适应运动目标的外观变化,为了解决CamShift算法在跟踪尺度变化的目标的不准确问题,本文在原算法基础上预测搜索窗口的尺寸,同时也对运动目标质心位置进行预测。实验结果表明,改进的算法对尺寸逐渐逐渐增大的目标都能自动选择合适的跟踪窗口大小。     

Visual tracking with randomly projected ferns

In this paper, a novel visual tracking method based on random ferns and random projection has been proposed. Instead of a binary comparison in the standard random ferns method, a subtraction is used in the proposed method. Different subtraction results are projected into a single real value by the random projection. The real value from the subtraction is more informative than a binary comparison, thus less subtractions are needed. More importantly, the proposed method has a constant memory resource requirement independent from the number of subtractions in each fern, whereas in a standard random ferns method, the requirement is an exponential relationship with the number of binary comparisons. Besides low memory requirement, our approach is also computational efficient. Experimental results demonstrate that the proposed method performs better than the standard random ferns method and some other methods, especially in memory and computing resources requirements, which are of vital importance in some embedded systems.     

可视化目标特征提取及动态跟踪算法验证系统

介绍了基于图像处理的目标特征提取和跟踪算法的验证、评估系统。该系统由图像预处理、目标分割、目标特征提取、特征稳定性分析和跟踪算法等模块构成,集成了诸多的典型算法,对不同的目标和背景进行处理,选择特定目标和背景的最优算法组合,并以动态跟踪形式对算法集进行验证。     

基于小波变换的遗传算法在红外跟踪系统中的应用

目前红外跟踪技术是当代红外技术的重要军事应用之一,但由于外界的干扰环境及红外热探测器件本身的灵敏度影响了其成像质量。为了提高红外跟踪系统的图像识别精度,本文针对红外探测器生成的空中目标图像,提出用基于小波变换的遗传算法对红外热图像进行处理。实验结果验证该算法可以有效提高图像质量,能够满足跟踪系统的精度要求。     

基于逻辑回归的口语理解方法研究

针对语音识别错误导致口语理解系统性能下降的问题,提出一种易于训练且解码快速的鉴别式口语理解方法。首先为每个语义要素建立一个二类逻辑回归模型,随后根据领域中的限制关系建立联合概率模型。在英语公开数据集DSTC2上的实验结果表明,该方法优于人工规则方法和语义元组分类器模型。     

红外多目标跟踪算法研究

介绍了红外搜索跟踪系统中多目标跟踪与航迹生成算法，采用极坐标数据进行数据的关联和滤波，采用基于逻辑的最近邻原则并结合目标的红外辐射特性进行航迹关联，实现多目标跟踪起始和终结。     

旋翼无人机视觉跟踪系统

设计实现了一种基于PC104计算平台的旋翼无人机自动目标跟踪系统.该系统由机载视觉子系统、地面站子系统、无线通信子系统3个部分组成.构建了完整的空地、人机交互环路.采用基于背景权重的Mean Shift目标跟踪算法,能够有效减小复杂环境背景对目标跟踪的影响.可靠性高且算法复杂度低.在室内外环境下进行的实验测试结果表明:系统在目标跟踪过程中即使遇到相似目标干扰或大面积遮挡,仍能够准确地自动跟踪目标,利用目标在图像中的位置主动引导数字云台与其保持相同运动方向,使目标尽可能处于相机中心视场范围内,验证了系统的可靠性和实时性.     

Visual tracking for intelligent vehicle-highway systems

The complexity and congestion of current transportation systems often produce traffic situations that jeopardize the safety of the people involved. These situations vary from maintaining a safe distance behind a leading vehicle to safely allowing a pedestrian to cross a busy street. Environmental sensing plays a critical role in virtually all of these situations. Of the sensors available, vision sensors provide information that is richer and more complete than other sensors, making them a logical choice for a multisensor transportation system. We propose robust detection and tracking techniques for intelligent vehicle-highway applications where computer vision plays a crucial role. In particular, we demonstrate that the controlled active vision framework can be utilized to provide a visual tracking modality to a traffic advisory system in order to increase the overall safety margin in a variety of common traffic situations. We have selected two application examples, vehicle tracking and pedestrian tracking, to demonstrate that the framework can provide precisely the type of information required to effectively manage the given traffic situation     

Visual object tracking with online sample selection via lasso regularization

In the past years, discriminative methods are popular in visual tracking. The main idea of the discriminative method is to learn a classifier to distinguish the target from the background. The key step is the update of the classifier. Usually, the tracked results are chosen as the positive samples to update the classifier, which results in the failure of the updating of the classifier when the tracked results are not accurate. After that the tracker will drift away from the target. Additionally, a large number of training samples would hinder the online updating of the classifier without an appropriate sample selection strategy. To address the drift problem, we propose a score function to predict the optimal candidate directly instead of learning a classifier. Furthermore, to solve the problem of a large number of training samples, we design a sparsity-constrained sample selection strategy to choose some representative support samples from the large number of training samples on the updating stage. To evaluate the effectiveness and robustness of the proposed method, we implement experiments on the object tracking benchmark and 12 challenging sequences. The experiment results demonstrate that our approach achieves promising performance.     

基于强跟踪AUKF的目标跟踪算法

针对无迹卡尔曼滤波器在递推过程中不具有对测量条件变化和系统模型不确定性的自适应性,在模型不准确或出现不良测量条件时跟踪效果不佳的问题,提出一种新的目标跟踪算法,即基于改进强跟踪的自适应无迹卡尔曼滤波器(STF-AUKF)。该算法一方面基于自适应滤波的思想,利用新息协方差匹配原理,建立对不良测量具有鲁棒性的自适应UKF;另一方面,依据改进强跟踪滤波的思想,采用时变渐消因子实时调节矩阵增益以此应对模型突变,保证跟踪效果。仿真结果表明,STF-AUKF算法在目标突发机动时仍然具有较好的稳定性和跟踪效果。     

Real-time algorithm for retinal tracking

Conventional retinal laser photocoagulation is presently performed by an ophthalmologist manually aiming a low-power laser beam at a desired site and firing a high-power laser for a preselected interval of time. To automate this process a retinal tracker must acquire a target, track small saccades, and identify loss of track during a large saccade. The authors successfully implemented a real-time algorithm that used a simple computer, video digitizing card, low light video camera, and fundus camera to perform rudimentary tracking on a photograph of a retina undergoing smooth circular motion. The algorithm tracked speeds up to 5 Hz, or 27°/s, which equated to the retina moving in a 525 μm diameter circle     

An algorithm for formant tracking

A formant tracking algorithm than first forms strings of spectral peaks considering only the relative positions of the peaks and then assigns these strings to the formants according to the relative positions and lengths of the strings is presented. An example is also given.     

概率加权质心跟踪算法研究

针对目前图像跟踪器跟踪不稳定、跟踪精度不高及不能满足实时性要求等问题,提出了一种概率加权的质心跟踪算法.该算法首先对波门内的像素进行阈值分割,摒弃灰度低于阈值的背景像素,保留目标像素的灰度值,然后计算波门内目标区域的质心.实验结果表明:基于概率加权的质心跟踪方法能够有效降低复杂背景和噪声干扰,增强跟踪系统的抗干扰能力,减少传统跟踪系统中使用大量灰度梯度值带来的巨大计算量,从而提高跟踪器的精度和稳定性.创新点在于通过引入概率加权的方法,在计算初始时刻的目标质心时使用贝叶斯概率作为权重,而没有设置离散的阈值来分辨目标,减少了传统跟踪系统中使用大量灰度梯度值产生的计算复杂度.     

Selfperturbing recursive least squares algorithm with fast tracking capability

A novel recursive least squares (RLS) type algorithm with a selfperturbing action is devised. The algorithm possesses a fast tracking capability in itself because its adaptation gain is automatically revitalised through perturbation of the covariance update dynamics by the filter output error square when it encounters sudden parameter changes. Furthermore, the algorithm converges to the true parameter values in stationary environments.<>     

Fast parameter tracking RLS algorithm with high noise immunity

A recursive least squares (RLS) based fast parameter tracking algorithm with high noise immunity is proposed. The fast parameter tracking capability of the algorithm is achieved by perturbing the covariance matrix update equation whenever the signal model parameters change. Since the perturbing terms depends on the auto- and crosscorrelations of the signal and algorithm outputs, the proposed algorithm is very robust with respect to noise. The efficiency of the algorithm has been verified by Monte-Carlo simulations.<>     

Visual tracking of robots in uncalibrated environments

This paper presents a new adaptive controller for visual tracking control of a robot manipulator in 3D general motion with a fixed camera whose intrinsic and extrinsic parameters are uncalibrated. In addition to camera parameters, the feature positions are also assumed unknown. Based on the fact that the unknown parameters appears linearly in the closed-loop dynamics of the system if the depth-independent interaction matrix is adopted to map the image errors onto the joint space of the manipulator, a new adaptive algorithm was developed to estimated the unknown parameters on-line. With a full consideration of dynamic responses of the robot manipulator, the Lyapunov method is employed to prove asymptotic convergence of the image errors. Simulation and experiment results are used to demonstrate the performance of the proposed approach.     

基于改进粒子滤波跟踪算法的运动视频跟踪

为了进一步提高目标跟踪的准确率,提出一种基于改进粒子滤波的运动视频跟踪算法。首先,通过高斯混合模型实现动态背景建模,从而降低噪声和局部动态背景的干扰;然后,在RGB颜色直方图分离的基础上,结合粒子滤波和迭代递归实现目标运动检测,提高了前景检测的准确性。仿真试验结果显示,相比典型粒子滤波算法、遗传粒子滤波和DCEM,改进粒子滤波跟踪算法得到的前景目标的轮廓更好,因此运动目标检测精确度更高且处理时间更短。