基于模糊聚类的半自动跟踪技术研究

现有半自动跟踪系统将操作员等效为准线性环节,增大了训练的难度和成本,不适应复杂战场环境下多个快速机动目标的选取和跟踪.利用特征提取方法得到视场的“潜在目标”集,引入模糊聚类方法,通过对操作响应进行分类,建立操控意图与目标运动特征之间的关系模型.通过充分的仿真试验,对比研究了跟踪平稳性、快速性、准确性等指标,结果说明该方法能够有效提升整体跟踪性能.     

基于反步自适应神经网络的船舶航迹控制

针对欠驱动船舶在稳定航速条件下轨迹跟踪问题,提出了一种基于自适应神经网络与反步法相结合的控制算法.该算法将实际的欠驱动船舶视为模型完全未知的非线性系统,利用神经网络的函数逼近特性实现控制器中非线性部分的在线估计,采用同时调整输入层-隐层、隐层-输出层间的权值阵的方法进行神经网络权值调整.通过选取积分型Lyapunov函数证明了闭环系统的稳定性.仿真实验表明该控制策略具有良好的跟踪特性,可以实现对期望航迹的精确跟踪.     

A variational method for contour tracking via covariance matching

This paper presents a novel formulation for contour tracking.We model the second-order statistics of image regions and perform covariance matching under the variational level set framework.Specifically,covariance matrix is adopted as a visual object representation for partial differential equation(PDE) based contour tracking.Log-Euclidean calculus is used as a covariance distance metric instead of Euclidean distance which is unsuitable for measuring the similarities between covariance matrices,because the matrices typically lie on a non-Euclidean manifold.A novel image energy functional is formulated by minimizing the distance metric between the candidate object region and a given template,and maximizing the one between the background region and the template.The corresponding gradient flow is then derived according to a variational approach,enabling partial differential equations(PDEs) based contour tracking.Experiments on several challenging sequences prove the validity of the proposed method.     

Joint spatial registration and multi-target tracking using an extended PM-CPHD filter

An extended product multi-sensor cardinalized probability hypothesis density (PM-CPHD) filter for spatial registration and multi-target tracking (MTT) is proposed. The number and states of targets and the biases of sensors are jointly estimated by this method without the data association. Monte Carlo (MC) simulation results show that the proposed method (i) outperforms, although computationally more expensive than, the extended multi-sensor PHD filter which has been proposed for joint spatial registration and MTT; (ii) outperforms the multi-sensor joint probabilistic data association (MSJPDA) filter which is also extended in this study for joint spatial registration and MTT when the clutter is relatively dense.     

An iterative linear quadratic regulator based trajectory tracking controller for wheeled mobile robot

We present an iterative linear quadratic regulator(ILQR) method for trajectory tracking control of a wheeled mobile robot system.The proposed scheme involves a kinematic model linearization technique,a global trajectory generation algorithm,and trajectory tracking controller design.A lattice planner,which searches over a 3D(x,y,θ) configuration space,is adopted to generate the global trajectory.The ILQR method is used to design a local trajectory tracking controller.The effectiveness of the proposed method is demonstrated in simulation and experiment with a significantly asymmetric differential drive robot.The performance of the local controller is analyzed and compared with that of the existing linear quadratic regulator(LQR) method.According to the experiments,the new controller improves the control sequences(v,ω) iteratively and produces slightly better results.Specifically,two trajectories,’S’ and ’8’ courses,are followed with sufficient accuracy using the proposed controller.     

基于小二乘-卡尔曼滤波的wMPS系统跟踪定位算法研究

wMPS(workspace Measuring Position System)是一种新型的基于平面交会原理的网络式测量系统,在静态测量研究的基础上,研究了动态跟踪测量的问题。为了减小运动引入的测量误差,利用运动方程提高测量精度,采用了最小二乘法对接收器的位置进行静态估算,将其结果作为伪观测值,然后利用卡尔曼滤波做进一步处理,该方法避免了观测方程中的非线性误差,实现了接收器在运动过程中的高精度跟踪定位。通过试验对最小二乘法和最小二乘-卡尔曼滤波法进行了比较,结果表明最小二乘-卡尔曼滤波法的估值精度远高于最小二乘法。     

运动目标检测与跟踪的DSP实现

研究了运动目标检测与跟踪的DSP（DigitalSignalProcessor）实现算法,以形心跟踪算法为整个处理系统的核心。采用目标形心跟踪算法,通过目标分割阶段的目标标记,如目标面积、周长、形心位置等信息的提取建立目标跟踪波门,实现目标的连续跟踪,并将此算法移植到sEED—VPM642硬件平台,实验结果表明能够达到预定目标。此外,为了克服形心算法的准确性和实时性缺陷,采用粒子滤波对算法进行必要的扩展,从MATLAB的仿真结果看,除个别采样点存在误差较大的情况,真实值曲线与粒子滤波跟踪曲线拟合较好。     

医用药剂中可见异物自动化检测研究

针对国内人工药品检测的低效率、低准确率的不足,文章介绍了一套根据国家行业标准设计的基于智能机器视觉的可见异物自动化检测系统。该系统主要包括机械传动模块、电气控制模块、图像识别与处理模块。根据异物运动连续性和噪声运动无序性等特点,提出了改进的三次差分和能量累积算法来提取运动杂质,然后采用基于基于SIFT特征匹配和最邻近匹配的目标跟踪算法对杂质进行跟踪,获取杂质的大小和数目并分析出杂质总类,最后检测出杂质情况。最后把检测结果与国家标准进行比对,判断出该药剂是否合格。测试表明,系统的检测准确率达90%以上,满足企业要求,基本能替代人工检测。     

Environmental Perception with Spatial Regularization Correlation Filter for Visual Tracking

With the introduction of correlation filtering (CF), the performance of visual object tracking is significantly improved. Circular shifts collecting samples is a key component of the CF tracker, and it also causes negative boundary effects. Most trackers add spatial regularization to alleviate boundary effects well. However, these trackers ignore the effect of environmental changes on tracking performance, and the filter discriminates poorly in the background interference. Here, to break these limitations, we propose a new correlation filter model, namely Environmental Perception with Spatial Regularization Correlation Filter for Visual Tracking. Specifically, we use the Average Peak to Correlation Energy (APCE) and the response value error between the two frames together to perceive environmental changes, which adjusts the learning rate to make the template more adaptable to environmental changes. To enhance the discriminatory capability of the filter, we use real background information as negative samples to train the filter model. In addition, the introduction of the regular term destroys the closed solution of CF, and this problem can be effectively solved by the use of the alternating direction method of multipliers (ADMM). Extensive experimental evaluations on three large tracking benchmarks are performed, which demonstrate the good performance of the proposed method over some of the state-of-the-art trackers.