基于Kanade-Lucas-Tomasi 算法的人脸特征点跟踪方法

与传统的在人面部画上标识点的特征点跟踪方法不同,KLT(Kanade-Lucas-Tclmasi)算法可以从未加标识点的正面人像视频系列中通过特征纹理信息直接获取面部某些特征点的位移,在KLT算法中加入了基于人脸统计信息的经验约束,使KLT算法更加合理有效。     

一种用于单目标跟踪的锚框掩码孪生RPN模型

针对孪生区域候选网络(RPN)易受干扰且目标丢失后无法跟踪的问题,引入锚框掩码网络机制,设计一种新型孪生RPN模型。设置多尺度模板图片,并将其与目标图片进行卷积操作,实现全图检测以避免目标丢失。通过对前三帧图片的IOU热度图进行学习,预测连续帧目标锚框掩码,简化计算并排除其他目标干扰。在VOT2016和OTB100数据集中的实验结果显示,该模型对VOT2016数据集检测帧率达到24.6 frame/s,预期平均覆盖率为0.344 5,对OTB100数据集的检测准确率和成功率分别为0.862和0.642。基于摄像头采集数据的目标丢失及干扰测试表明,该模型具有良好的抗干扰性与实时性。     

S7-300控制系统中大量报警点的监控系统程序设计

大型化工反应过程控制系统中报警点非常多,用一般方法,监控程序设计中对大量报警点的处理比较繁琐。介绍了一种基于STEP7 V5.4和WinCC Flexible对S7-300控制系统中大量报警点监控的处理方法。     

排除光流错误跟踪点的鲁棒方法

针对光流法用于跟踪光照变化和部分遮挡情况下的物体容易产生漂移的问题,通过比较跟踪点与该点补集映射关系产生的投影点之间的距离,提出了一种光流错误跟踪点排除方法——异类距法.首先证明了异类距排除误差最大元素的正确性;然后在静止场景受光照变化和部分遮挡情况下,给出了异类距排除错误跟踪点以及摄像机姿态矩阵在时间序列上的分布.针对视频序列的抖动情况,与传统方法进行比较的实验结果表明,该方法对于排除错误跟踪点是鲁棒的.     

Vision Based Tracking for a Quadrotor Using Vanishing Points

In this paper, a vision based line tracking control strategy for mini-rotorcraft is presented. In order to estimate the 3-D position of the mini-rotorcraft over the trajectory a vanishing points technique is used. A dynamic model is derived employing the Newton–Euler approach and a nonlinear controller to stabilize, in closed-loop system, this mathematical model is proposed. To validate the theoretical results, a real-time embedded control system has been developed. The performance of the vision and control algorithms has been tested when the helicopter has tracked a line painted in a wall. The experimental results have shown the good behavior of the control laws.     

基于自动标注特征点的人体关节跟踪

现有的基于人体特征点的关节跟踪方法大多采用手动标注特征点方式;提出了一种新的特征跟踪的方法,该方法能够对目标人体关节特征点进行自动标注,实现对人体关节点的自动跟踪;首先采用帧间差分和背景差分相融合的方法来分割运动人体,采用CANNY算子提取目标轮廓,自动标注关节特征点;然后采用LK光流算法跟踪标注的特征点;最后利用卡尔曼滤波线性跟踪来预测特征点出现的位置,从而修正那些产生跟踪错误的特征点;实验结果表明,该方法能够有效的对实际视频的目标人体进行特征点自动标注,取得较好的跟踪效果.     

一种复杂环境中WSN锚节点的布置算法研究

在无线传感器网络的定位研究中,现有的节点布置算法都是在不考虑障碍物存在的理想情况下提出的.本文构建出存在障碍物的二维空间模型,并提出一种无线传感器网络锚节点布置算法DABTG(Deployment of Anchor-points Based Barrierd Triangle Grid).该算法在三角形网格的基础上,有序增加节点数量,在节点覆盖区域的交点处放置节点.仿真表明,算法DABTG在有障碍物存在的情况下可达到被测区域的完全覆盖.     

一种运动目标多特征点的鲁棒跟踪方法研究

提出了一种基于特征光流分割和卡尔曼滤波估计的鲁棒性的运动目标跟踪方法。该方法具有很多特点：首先在特征光流的计算中采用由粗到细的层级匹配算法,因而能够计算大的运动速度和具有更好的匹配精度;其次采用了有效的遮挡判决算法,该算法综合利用了先验的信息,对噪声的干扰不敏感;最后建立了线性卡尔曼滤波模型,当特征点被遮挡或丢失时,能够预测它们的位置,这使得跟踪更具有主动性。实验表明,该方法具有高精度、快速跟踪和很好的鲁棒性。     

一种基于图像序列的脑电极点的跟踪定位方法

为了对脑电检测诊断中的各脑电极点进行自动定位跟踪,该文提出了一种基于图像序列的自动定位方法,首先在单帧图像中,根据控制点的标识,确定首个基准点,之后根据基准点与局部周围电极之间的相互关系,采用动态迭代搜索方法,获得后续的置信基准点及周围电极点的标识名。实验结果表明,该方法对旋转拍摄时角度的随机变动,以及镜头的仰俯变动均具有很好的适应性,可以准确地给出同一个电极在不同帧中的位置匹配关系。     

Tracking multiple features using relaxation

A new algorithm is introduced for tracking multiple features in an image sequence. First, the proposed method iteratively reduces the disparity of each possible match by relaxation labeling. It is assumed that all trajectories are smooth and the smoothness is used as the measure for correspondence. Some cases of wrong correspondences can be recovered by a proposed scheme called constraint-aided exchange during the tracking process. Occluded or missing feature points can be detected and predicted in the proposed algorithm. Finally, the algorithm is applied to data obtained from real world scenes. The human motion analysis can be achieved by the tracking algorithm.     

Tracking moving optima using Kalman-based predictions

The dynamic optimization problem concerns finding an optimum in a changing environment. In the field of evolutionary algorithms, this implies dealing with a time-changing fitness landscape. In this paper we compare different techniques for integrating motion information into an evolutionary algorithm, in the case it has to follow a time-changing optimum, under the assumption that the changes follow a nonrandom law. Such a law can be estimated in order to improve the optimum tracking capabilities of the algorithm. In particular, we will focus on first order dynamical laws to track moving objects. A vision-based tracking robotic application is used as testbed for experimental comparison.     

Mass-Spring Simulation using Adaptive Non-Active Points

This paper introduces an adaptive component to a mass-spring system as used in the modelling of cloth for computer animation. The new method introduces non-active points to the model which can adapt the shape of the cloth at inaccuracies. This improves on conventional uniform mass-spring systems by producing more visually pleasing results when simulating the drape of cloth over irregular objects. The computational cost of simulation is decreased by reducing the complexity of collision handling and enabling the use of coarser mass-spring networks.     

Neural Network State Learning Based Adaptive Terminal ILC for Tracking Iteration-varying Target Points

Terminal iterative learning control(TILC) is developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations under strictly identical initial conditions. In this work, the initial states are not required to be identical further but can be varying from iteration to iteration. In addition, the desired terminal point is not fixed any more but is allowed to change run-to-run. Consequently, a new adaptive TILC is proposed with a neural network initial state learning mechanism to achieve the learning objective over iterations. The neural network is used to approximate the effect of iteration-varying initial states on the terminal output and the neural network weights are identified iteratively along the iteration axis.A dead-zone scheme is developed such that both learning and adaptation are performed only if the terminal tracking error is outside a designated error bound. It is shown that the proposed approach is able to track run-varying terminal desired points fast with a specified tracking accuracy beyond the initial state variance.     

Human Motion Tracking by Registering an Articulated Surface to 3D Points and Normals

We address the problem of human motion tracking by registering a surface to 3-D data. We propose a method that iteratively computes two things: Maximum likelihood estimates for both the kinematic and free-motion parameters of an articulated object, as well as probabilities that the data are assigned either to an object part, or to an outlier cluster. We introduce a new metric between observed points and normals on one side, and a parameterized surface on the other side, the latter being defined as a blending over a set of ellipsoids. We claim that this metric is well suited when one deals with either visual-hull or visual-shape observations. We illustrate the method by tracking human motions using sparse visual-shape data (3-D surface points and normals) gathered from imperfect silhouettes.     

Tracking humoral responses using self assembling protein microarrays

The humoral immune response is a highly specific and adaptive sensor for changes in the body's protein milieu, which responds to novel structures of both foreign and self antigens. Although Igs represent a major component of human serum and are vital to survival, little is known about the response specificity and determinants that govern the human immunome. Historically, antigen-specific humoral immunity has been investigated using individually produced and purified target proteins, a labor-intensive process that has limited the number of antigens that have been studied. Here, we present the development of methods for applying self-assembling protein microarrays and a related method for producing 96-well formatted macroarrays for monitoring the humoral response at the proteome scale. Using plasmids encoding full-length cDNAs for over 850 human proteins and 1700 pathogen proteins, we demonstrate that these microarrays are highly sensitive, specific, reproducible, and can simultaneously measure immunity to thousands of proteins without a priori protein purification. Using this approach, we demonstrate the detection of humoral immunity to known and novel self-antigens, cancer antigens, autoimmune antigens, as well as pathogen-derived antigens. This represents a powerful and versatile tool for monitoring the immunome in health and disease.     

Tracking non-rigid objects using probabilistic Hausdorff distance matching

This paper proposes a new method of extracting and tracking a non-rigid object moving against a cluttered background while allowing camera movement. For object extraction we first detect an object using watershed segmentation technique and then extract its contour points by approximating the boundary using the idea of feature point weighting. For object tracking we take the contour to estimate its motion in the next frame by the maximum likelihood method. The position of the object is estimated using a probabilistic Hausdorff measurement while the shape variation is modelled using a modified active contour model. The proposed method is highly tolerant to occlusion. Unless an object is fully occluded during tracking, the result is stable and the method is robust enough for practical application.     

Tracking a maneuvering target using neural fuzzy network

A fast target maneuver detecting and highly accurate tracking technique using a neural fuzzy network based on Kalman filter is proposed in this paper. In the automatic target tracking system, there exists an important and difficult problem: how to detect the target maneuvers and fast response to avoid miss-tracking? The traditional maneuver detection algorithms, such as variable dimension filter (VDF) and input estimation (IE) etc., are computation intensive and difficult to implement in real time. To solve this problem, neural network algorithms have been issued recently. However, the normal neural networks such as backpropagation networks usually produce the extra problems of low convergence speed and/or large network size. Furthermore, the way to decide the network structure is heuristic. To overcome these defects and to make use of neural learning ability, a developed standard Kalman filter with a self-constructing neural fuzzy inference network (KF-SONFIN) algorithm for target tracking is presented in this paper. By generating possible target trajectories including maneuver information to train the SONFIN, the trained SONFIN can detect when the maneuver occurred, the magnitude of maneuver values and when the maneuver disappeared. Without having to change the structure of Kalman filter nor modeling the maneuvering target, this new algorithm, SONFIN, can always find itself an economic network size with a fast learning process. Simulation results show that the KF-SONFIN is superior to the traditional IE and VDF methods in estimation accuracy.     

Tracking people in video sequences using multiple models

This paper presents a multiple model real-time tracking technique for video sequences, based on the mean-shift algorithm. The proposed approach incorporates spatial information from several connected regions into the histogram-based representation model of the target, and enables multiple models to be used to represent the same object. The use of several regions to capture the color spatial information into a single combined model, allow us to increase the object tracking efficiency. By using multiple models, we can make the tracking scheme more robust in order to work with sequences with illumination and pose changes. We define a model selection function that takes into account both the similarity of the model with the information present in the image, and the target dynamics. In the tracking experiments presented, our method successfully coped with lighting changes, occlusion, and clutter.