遮挡条件下多运动车辆跟踪研究

针对SURF算法能够提取到的图像特征点较少的问题,基于保持亮度特性的双直方图均衡算法,通过重构SURF尺度空间提取图像特征。将这种方法与卡尔曼滤波相结合进行目标跟踪,用特征点的中心作为跟踪点;通过卡尔曼滤波预测出运动目标的位置,判断遮挡是否发生;最后,应用该方法进行目标特征向量匹配。实验结果表明,该算法对发生旋转、缩放以及遮挡的多运动目标都可进行稳定跟踪,其跟踪速度比R-SURF算法提高20%;在跟踪速度相当的情况下,跟踪精度要高于卡尔曼滤波跟踪算法。     

联合YOLO和Camshift的目标跟踪算法研究

为了解决传统目标跟踪算法在有遮挡后无法准确跟踪的问题,提出了将YOLO和Camshift算法相联合的目标跟踪算法.基于YOLO网络结构来构建目标检测的模型,在模型构建之前,采用图像增强的方法对视频帧进行预处理,在保留视频帧中足够图像信息的同时,提高图像质量,降低YOLO算法的时间复杂度.用YOLO算法确定出目标,完成对目标跟踪的初始化.根据目标的位置信息使用Camshift算法对后续的视频帧进行处理,并对每一帧的目标进行更新,从而可以保证不断调整跟踪窗口位置,适应目标的移动.实验结果表明,所提的方法能够有效地克服目标被遮挡后跟踪丢失的问题,具有很好的鲁棒性.     

智能车辆的滑模轨迹跟踪控制

智能车辆是集各种机械装置、传感器、计算机于一体的复杂非线性系统,其轨迹跟踪控制器是研究的关键技术之一。针对高速自主导航智能车辆轨迹跟踪控制器鲁棒性、精确性和实时性的高要求,在智能车辆结构组成与运动模型基础上,设计了一种滑模变结构控制器。通过控制智能车辆的线速度和角速度实现智能车辆对任意路径的跟踪,并用Matlab进行了仿真实验。结果验证了该方法的有效性和可靠性。     

KCF算法在车辆目标跟踪上的参数配置研究

对地面车辆目标的视觉跟踪任务首要是满足实时性,其次是在复杂背景下对目标跟踪的鲁棒性。KCF算法作为经典的判别式跟踪算法,凭借其高效的跟踪器学习效率,一直作为主流的实时跟踪算法之一。其中,搜索区域的大小选取在很大程度上决定了能否生成稳定的跟踪器,然而对于不同尺寸的车辆目标,其最优的搜索区域大小通常是不同的。为此,本文以标准数据集OTB2015作为车辆目标视频源,通过分辨率降采样来模拟多组不同尺寸的目标运动场景,论证在不同距离下实现最优车辆跟踪的KCF算法参数配置,为长距离的车辆跟踪任务提供了参数依据。     

基于Camshift与Kalman的目标跟踪算法

针对目标跟踪复杂的难点,提出了一种比较实用的跟踪方法。采用基于颜色概率分布的Camshift算法进行目标跟踪的同时,引入卡尔曼滤波,并给出模型参数。在目标发生遮挡时,使用卡尔曼滤波对目标运动状态进行估计。实验表明,算法能够对目标进行持续、稳定的跟踪。     

基于mean-shift算法的人脸实时跟踪方法*

复杂环境下实时人脸跟踪方法在视觉监控系统中具有很重要的意义,但目前的跟踪算法普遍存在目标遮挡、尺寸变化等过于敏感的不足,限制了其应用范围。提出了一种人脸检测、mean-shift算法与卡尔曼滤波器相结合的实时全自动人脸跟踪算法。实验结果表明该算法实时性很强,可以实现对运动人脸的快速跟踪,同时对目标遮挡也有很好的鲁棒性。     

基于mean-shift算法的人脸实时跟踪方法*

复杂环境下实时人脸跟踪方法在视觉监控系统中具有很重要的意义,但目前的跟踪算法普遍存在目标遮挡、尺寸变化等过于敏感的不足,限制了其应用范围。提出了一种人脸检测、mean-shift算法与卡尔曼滤波器相结合的实时全自动人脸跟踪算法。实验结果表明该算法实时性很强,可以实现对运动人脸的快速跟踪,同时对目标遮挡也有很好的鲁棒性。     

团块与Mean-Shift结合的局部遮挡目标跟踪

传统的基于Mean-Shift的目标跟踪方法利用目标的全局特征进行跟踪,在局部遮挡情况下跟踪效果不佳。提出一种基于团块建模和Mean-Shift相结合的利用目标局部特征的运动目标跟踪方法,对目标进行团块建模,利用Mean-shift算法对各团块进行跟踪,在此基础上确定目标新位置。该方法能够在目标发生局部遮挡时,自动选取未被遮挡的团块的跟踪结果来确定目标的位置。为了提高方法对背景干扰的鲁棒性,采用背景加权的Mean-Shift算法。实验结果表明：该方法在局部遮挡的情况下可较好地进行目标跟踪,跟踪效果优于报导的基于Mean-Shift的方法。     

适用于遮挡问题的目标跟踪算法

提出一种基于网格模型的目标跟踪算法．该算法首先进行遮挡区域检测，然后进行网格结点的运动估计和网格更新过程完成目标的多帧跟踪．改进的遮挡区域检测算法有效地提高了检测准确度，从而确保遮挡区域的准确跟踪；网格结点的运动估计是通过特征窗口运动补偿匹配完成，可以有效地克服块效应．实验证明，该算法解决了二维运动估计时网格模型在遮挡区域存在的问题，并可以有效地进行目标准确跟踪．     

一种利用时空约束轨迹片段关联的目标跟踪方法*

目标跟踪是智能监控系统中的一个重要的研究领域。由于检测不准确,目标部分或者整体遮挡会造成检测失败。针对以上问题提出一种利用时空约束的轨迹片段关联方法实现对目标的跟踪。首先通过减背景方法检测到移动目标,然后生成轨迹片断,最后通过计算轨迹片段的时空连续性关联轨迹片段,找到最符合时空连续性的轨迹片段关联。通过实验证明方法可以有效解决遮挡,误检测以及目标合并分离问题。     

遮挡目标的分片跟踪处理

遮挡是目标跟踪领域一个棘手的问题,如何处理好遮挡是衡量跟踪算法鲁棒性的关键。本文就此问题,提出了一种基于分片跟踪下的遮挡处理算法。本算法在目标发生部分遮挡或者形变后,通过剩余有效片的强度信息,继续对目标实现可靠跟踪,并结合卡尔曼滤波有效的处理跟踪过程中的遮挡。算法采用分片的思想,利用Bhattacharyya系数作为候选目标片与相应模板片的相似性度量,有效的跟踪目标,采用H分量的反向投影的方法辨别遮挡和形变,根据遮挡的不同类型,做相应的处理,实现对目标的鲁棒性跟踪,本实验就遮挡提出了关联性遮挡和非关联性遮挡的概念,增加算法跟踪的可靠性。     

遮挡情况下的视觉目标跟踪方法研究

将目标整体相关匹配算法和目标各子块相关匹配作表决的算法相结合,有效解决了运动目标被遮挡的跟踪问题.目标被遮挡,表现为某些子块被遮挡且匹配错误.对被遮挡的子块使其不参与表决,也不参与整体相关匹配的计算,只利用目标剩余的能代表目标本身属性的未遮挡子块继续跟踪目标.实验结果表明,采用的两种算法互为补充,对解决遮挡情况下目标的视觉跟踪是有效的.     

Nonlinear feedback control and trajectory tracking of vehicle

This paper mainly studies nonlinear feedback control applied to the nonlinear vehicle dynamics with varying velocity. The main objective of this study is the stabilisation of longitudinal, lateral and yaw angular vehicle velocities. To this end, a nonlinear vehicle model is developed which takes both the lateral and longitudinal vehicle dynamics into account. Based on this model, a method to build a nonlinear state feedback control is first designed by which the complexity of system structure can be simplified. The obtained system is then synthesised by the combined Lyapunov–LaSalle method. The simulation results show that the proposed control can improve stability and comfort of vehicle driving. Moreover, this paper presents a lemma which ensures the trajectory tracking and path-following problem for vehicle. It can also be exploited simultaneously to solve both the tracking and path-following control problems of the vehicle ride and driving stability. We also show how the results of the lemma can be applied to solve the path-following problem, in which the vehicle converges and follows a designed path. The effectiveness of the proposed lemma for trajectory tracking is clearly demonstrated by simulation results.     

On controllability and trajectory tracking of a kinematic vehicle model

This paper presents some further results concerning the issues of controllability and trajectory tracking regarding a front-wheel drive vehicle kinematic model. A simple procedure for computing an open-loop control strategy that transfers the system between given initial and final states, is presented. In particular, the input function is computed by means of a set of linear algebraic equations. The resulting motion planning procedure allows us to present a control scheme for solving the trajectory (a time-parameterized reference signal) tracking problem. Various applications of the approach in forward and backward motions are considered, and simulation results are presented.     

煤矿井下视频多目标轨迹跟踪算法研究

为了提高煤矿井下监控视频的目标识别准确率,对运动目标进行有效跟踪,将小波变换和背景差分法相结合,对Camshift算法进行改进,提出了适用于煤矿井下视频多目标轨迹跟踪算法。首先采用小波三层变换对视频图像进行去噪处理,得到低频图像。然后再进行背景差分运算,检测出运动目标。最后采用Camshift算法对运动目标进行跟踪处理。实验结果表明,改进的Camshift算法减少了原始Camshift算法在初始候选目标时的随机性,提高了目标检测和跟踪的准确率,为煤矿的安全生产提供了保证。     

基于改进的均值漂移算法的目标跟踪

提出了一种基于目标颜色特征的改进的均值漂移算法，对符合颜色模板的目标点不论其在直方图中的概率大小，都赋予相同的最大权值，使目标最大限度地成为密度极值区，以克服干扰影响，并提出了一种分块检测遮挡算法，遮挡期间不更新颜色模板，以保证遮挡后恢复准确的跟踪。实验结果表明该算法具有较强的鲁棒性，能有效实现复杂场景下的目标跟踪。     

HELOP: Multi-target tracking based on heuristic empirical learning algorithm and occlusion processing

Multi-target tracking is one of the important fields in computer vision, which aims to solve the problem of matching and correlating targets between adjacent frames. In this paper, we propose a fine-grained track recoverable (FGTR) matching strategy and a heuristic empirical learning (HEL) algorithm. The FGTR matching strategy divides the detected targets into two different sets according to the distance between them, and adopts different matching strategies respectively, in order to reduce false matching, we evaluated the trust degree of the target’s appearance feature information and location feature information, adjusted the proportion of the two reasonably, and improved the accuracy of target matching. In order to solve the problem of trajectory drift caused by the cumulative increase of Kalman filter error during the occlusion process, the HEL algorithm predicts the position information of the target in the next few frames based on the effective information of other previous target trajectories and the motion characteristics of related targets. Make the predicted trajectory closer to the real trajectory. Our proposed method is tested on MOT16 and MOT17, and the experimental results verify the effectiveness of each module, which can effectively solve the occlusion problem and make the tracking more accurate and stable.     

基于RFID技术的四旋翼无人机轨迹跟踪控制系统设计

为促进四旋翼无人机的飞行自主性,增强无人监管情况下飞行器主机所具备的避障行进能力,设计基于RFID技术的四旋翼无人机轨迹跟踪控制系统;采用RFID标签识别技术,调制处理既定控制信号,利用标签识别协议,连接微型四旋翼轨迹控制器与内环姿态控制器,通过数据通信链路,提取轨迹跟踪控制所需的传输电子量,完成轨迹跟踪控制系统硬件设计;利用动力系统中的参数辨识策略,确定与轨迹姿态控制相关的物理规律标注,实现四旋翼无人机轨迹跟踪控制;实验结果表明,与机器视觉型控制系统相比,基于RFID技术的控制系统的SSI避障行进指标数值相对较高,全局最大值达到了 79％,四旋翼无人机滚转角平均值为85°,能够有效抑制四旋翼无人机滚转角的数值上升趋势,增强无人监管情况下飞行器主机避障行进能力.     

An evolutionary particle filter with the immune genetic algorithm for intelligent video target tracking

Particle filter algorithm is widely used for target tracking using video sequences, which is of great importance for intelligent surveillance applications. However, there is still much room for improvement, e.g. the so-called “sample impoverishment”. It is brought by re-sampling which aims to avoid particle degradation, and thus becomes the inherent shortcoming of the particle filter. In order to solve the problem of sample impoverishment, increase the number of meaningful particles and ensure the diversity of the particle set, an evolutionary particle filter with the immune genetic algorithm (IGA) for target tracking is proposed by adding IGA in front of the re-sampling process to increase particle diversity. Particles are regarded as the antibodies of the immune system, and the state of target being tracked is regarded as the external invading antigen. With the crossover and mutation process, the immune system produces a large number of new antibodies (particles), and thus the new particles can better approximate the true state by exploiting new areas. Regulatory mechanisms of antibodies, such as promotion and suppression, ensure the diversity of the particle set. In the proposed algorithm, the particle set optimized by IGA can better express the true state of the target, and the number of meaningful particles can be increased significantly. The effectiveness and robustness of the proposed particle filter are verified by target tracking experiments. Simulation results show that the proposed particle filter is better than the standard one in particle diversity and efficiency. The proposed algorithm can easily be extended to multiple objects tracking problems with occlusions.     

3D head tracking under partial occlusion

A new algorithm for 3D head tracking under partial occlusion from 2D monocular image sequences is proposed. The extended superquadric (ESQ) is used to generate a geometric 3D face model in order to reduce the shape ambiguity during tracking. Optical flow is then regularized by this model to estimate the 3D rigid motion. To deal with occlusion, a new motion segmentation algorithm using motion residual error analysis is developed. The occluded areas are successfully detected and discarded as noise. Furthermore, accumulation error is heavily reduced by a new post-regularization process based on edge flow. This makes the algorithm more stable over long image sequences. The algorithm is applied to both synthetic occlusion sequence and real image sequences. Comparisons with the ground truth indicate that our method is effective and is not sensitive to occlusion during head tracking.