体育视频序列中基于IMM的运动目标跟踪算法

在视频处理领域的运动目标跟踪问题中,卡尔曼滤波器（KF）与扩展卡尔曼滤波器（EKF）已经得到了广泛的应用,但在复杂背景或是目标高机动运动的情况下跟踪效果并不理想。提出一种基于交互多模型算法（IMM）,并采用去偏转换测量卡尔曼滤波器（CMKF-D）对运动目标进行跟踪的算法。该算法有效地解决了单一模型无法与运动特性相匹配的问题,并克服了KF、EKF对非线性模型线性化所引入的误差。以足球视频为例进行仿真实验,结果表明该算法有效地提高了视频序列中运动目标跟踪的准确率。     

全局运动补偿的动态背景下运动轨迹跟踪算法

针对动态背景下,一般跟踪算法存在着被动跟踪的滞后或偏移的问题,提出了一种结合Kalman滤波器的Mean-Shift跟踪算法.对运动矢量进行预处理,得到一个平稳更能反映运动信息的矢量场;利用Mean-Shift搜索算法精确地确定对象位置;此基础上,利用Kalman滤波器算法进行运动估计预测,来确定运动的轨迹.实验表明:与现有的方法相比,该方法可从复杂场景中更准确地对运动对象进行轨迹的跟踪.     

Lie群下利用改进JPDA滤波器的智能车立体视觉多目标跟踪方法

提出基于改进联合概率数据关联滤波器的智能车立体视觉多目标跟踪方法。利用立体视觉摄像头采集车辆及行人图像、视频;在Lie群下对传感器的不确定性进行建模,并采用欧几里德群算法对预处理的图像进行状态滤波;在可能存在车辆的区域内利用双目视觉去除误检,并获得车辆的位置信息;通过卡尔曼滤波器对测量的不确定度和预测目标运动的轨迹进行确认;运用改进的联合概率数据关联滤波器对车辆及行人的跟踪结果进行优化修正。实验结果表明,提出的方法可以有效解决智能车多目标跟踪问题,大幅度提升驾驶系统的自动化和智能化水平。相比其他较新的目标跟踪方法,提出的方法在跟踪精度和速度上具有明显的优势,且在跟踪车辆时不会产生明显的偏移、不会遗漏对行人的跟踪。     

一种多运动目标检测、跟踪方法研究与实现*

针对固定监控场景提出了一种运动目标检测与跟踪方案。在运动目标检测中,利用像素梯度及色度均值、方差分布建立并实时更新背景模型。在目标跟踪模块,引入卡尔曼滤波器预测目标参数,合并目标碎片,建立帧间目标匹配矩阵完成目标匹配。通过实际图像序列测试,算法能较好地实现运动目标跟踪,获得运动目标的轨迹,具有良好的实时性和适应环境变化的能力。     

基于改进运动历史图像的多运动目标实时跟踪

提出了一种利用视频图像对运动目标进行实时检测与跟踪的新方法.该方法利用基于改进的时间片的运动历史图像(tMHI)的灰度阶梯轮廓方法对多个运动目标进行检测,通过卡尔曼滤波器对多目标进行跟踪,并得到了各个运动目标的轨迹曲线,进而实现了对视频图像中多目标的跟踪.同时,该方法对多个目标的遮挡问题获得了明显的改善效果.实验结果表明,该方法能够对复杂场景下的多个目标进行有效的识别和准确的跟踪,系统的实时性强,识别率高,而且该方法对于复杂视频监视系统场景中的光照变化、雨雾等干扰具有较强的稳健性.     

基于改进的稀疏重构算法的行人异常行为分析

针对视频监控中行人异常行为识别问题,首先对行人进行跟踪,然后对跟踪得到的轨迹进行分析,最后判断行人行为是否存在异常。在行人跟踪方面,在时空上下文跟踪算法的基础上结合卡尔曼滤波器,有效改进了复杂背景中的遮挡问题。在异常分析方面,将跟踪得到的目标轨迹按照轨迹形状进行分类,得到场景中的正常轨迹集;将这些轨迹集作为后续处理的训练样本集,通过改进的稀疏重构算法对轨迹进行分析,利用重构误差来判断异常。五段视频序列的测试结果表明,该方法与改进前的方法相比具有较高的识别率。     

遮挡情况下的多目标跟踪算法*

在视频监控系统中,由于背景的复杂变化,运动目标经常会出现部分或全部被遮挡的情况。为了在遮挡条件下进行多目标跟踪,针对运动目标发生遮挡情况下的Mean Shift跟踪问题进行了研究,提出一种新的抗遮挡算法。利用卡尔曼滤波器来获得每帧Mean Shift算法的起始位置,再利用Mean Shift算法得到目标跟踪位置,通过目标遮挡判定机制和目标搜索机制来解决遮挡问题。实验表明,该算法较好地解决了运动目标的遮挡问题。     

一种稳健的移动机器人目标跟踪算法

为了有效地实现复杂环境下机器人运动目标跟踪,提出了一种结合卡尔曼滤波和均值漂移的目标跟踪算法.该算法首先通过帧间差法在复杂背景中获取目标模型,以机器人自身一个周期的运动作为卡尔曼滤波器的输入量,以卡尔曼滤波器的估计值作为均值漂移算法的启动点,再利用均值漂移算法得到最终目标位置,最后通过目标遮挡判定来解决遮挡问题.实验表...     

一种新的模糊-卡尔曼滤波器的控制算法及应用

针对高速剪切中管材运动需要精确而快速的预测问题,提出了一种新的模糊-卡尔曼滤波器:将过程噪声、量测噪声与残差均值和残差方差之间分别建立模糊曲面;省略模糊规则推理;将滤波器的采样时间与剪切误差之间的关系模糊化.以这样结构的滤波器来提高对剪切误差的跟踪和预测能力.仿真结果显示,新的模糊-卡尔曼滤波器比传统的卡尔曼滤波器更快和更稳定地将剪切误差进行抑制,而且剪切精度得到提高.     

基于轨迹的足球检测与跟踪

在足球比赛视频中对足球进行检测和跟踪,如果球与球场线融合或球与球员形成连通区域,则得到球的位置信息将变得非常困难.针对这类问题提出了一种基于轨迹的解决方案.基于维特比算法检测并跟踪球,应用最小二乘法得到球运动轨迹的插值函数,基于所得到的插值函数,剔除维特比算法检测与跟踪球过程中的一些误检,同时补充漏检处的球位置信息.通过实验验证,算法具有较高的准确性.     

A novel framework for motion segmentation and tracking by clustering incomplete trajectories

In this paper, we present a framework for visual object tracking based on clustering trajectories of image key points extracted from an image sequence. The main contribution of our method is that the trajectories are automatically extracted from the image sequence and they are provided directly to a model-based clustering approach. In most other methodologies, the latter constitutes a difficult part since the resulting feature trajectories have a short duration, as the key points disappear and reappear due to occlusion, illumination, viewpoint changes and noise. We present here a sparse, translation invariant regression mixture model for clustering trajectories of variable length. The overall scheme is converted into a maximum a posteriori approach, where the Expectation–Maximization (EM) algorithm is used for estimating the model parameters. The proposed method detects the different objects in the input image sequence by assigning each trajectory to a cluster, and simultaneously provides their motion. Numerical results demonstrate the ability of the proposed method to offer more accurate and robust solutions in comparison with other tracking approaches, such as the mean shift tracker, the camshift tracker and the Kalman filter.     

Trajectory-Based Ball Detection and Tracking in Broadcast Soccer Video

This paper presents a novel trajectory-based detection and tracking algorithm for locating the ball in broadcast soccer video (BSV). The problem of ball detection and tracking in BSV is well known to be very challenging because of the wide variation in the appearance of the ball over frames. Direct detection algorithms do not work well because the image of the ball may be distorted due to the high speed of the ball, occlusion, or merging with other objects in the frame. To overcome these challenges, we propose a two-phase trajectory-based algorithm in which we first generate a set of ball-candidates for each frame, and then use them to compute the set of ball trajectories. Informally, the two key ideas behind our strategy are 1) while it is very challenging to achieve high accuracy in locating the precise location of the ball, it is relatively easy to achieve very high accuracy in locating the ball among a set of ball-like candidates and 2) it is much better to study the trajectory information of the ball since the ball is the "most active" object in the BSV. Once the ball trajectories are computed, the ball locations can be reliably recovered from them. One important advantage of our algorithm is that it is able to reliably detect partially occluded or merged balls in the sequence. Two videos from the 2002 FIFA World Cup were used to evaluate our algorithm. It achieves a high accuracy of about 81% for ball location     

Ball tracking and 3D trajectory approximation with applications to tactics analysis from single-camera volleyball sequences

Providing computer-assisted tactics analysis in sports is a growing trend. This paper presents an automatic system for ball tracking and 3D trajectory approximation from single-camera volleyball sequences as well as demonstrates several applications to tactics analysis. Ball tracking in volleyball video has great complexity due to the high density of players on the court and the complicated overlapping of ball-player. The 2D-to-3D inference is intrinsically challenging due to the loss of 3D information in projection to 2D frames. To overcome these challenges, we propose a two-phase ball tracking algorithm in which we first detect ball candidates for each frame, and then use them to compute the ball trajectories. With the aid of camera calibration, we involve physical characteristics of ball motion to approximate the 3D ball trajectory from the 2D trajectory. The visualization of 3D trajectory and the applications to trajectory-based tactics analysis not only assist the coaches and players in game study but also make game watching a whole new experience. The experiments on international volleyball games show encouraging results. We believe that the proposed framework can be extended and applied to various kinds of sports games.     

Tracking the soccer ball using multiple fixed cameras

This paper demonstrates innovative techniques for estimating the trajectory of a soccer ball from multiple fixed cameras. Since the ball is nearly always moving and frequently occluded, its size and shape appearance varies over time and between cameras. Knowledge about the soccer domain is utilized and expressed in terms of field, object and motion models to distinguish the ball from other movements in the tracking and matching processes. Using ground plane velocity, longevity, normalized size and color features, each of the tracks obtained from a Kalman filter is assigned with a likelihood measure that represents the ball. This measure is further refined by reasoning through occlusions and back-tracking in the track history. This can be demonstrated to improve the accuracy and continuity of the results. Finally, a simple 3D trajectory model is presented, and the estimated 3D ball positions are fed back to constrain the 2D processing for more efficient and robust detection and tracking. Experimental results with quantitative evaluations from several long sequences are reported.     

Localization of a wheeled mobile robot by sensor data fusion based on a fuzzy logic adapted Kalman filter

Accurate estimates of mobile robot location, if available, can be used to improve the performance of a vehicle dynamics control system. To this purpose, the data provided by odometric and sonar sensors are here fused together by means of an extended Kalman filter, providing robot position and orientation estimates at each sampling instant. To cope with the tracking of long trajectories, the performance of the filter is improved by introducing an on-line fuzzy-rule-based adaptation scheme.     

基于深度学习的多目标运动轨迹预测算法

针对多目标运动轨迹预测过程中由于检测精度和实时性不足造成部分目标位置信息丢失和预测准确度不高问题,提出基于改进卡尔曼滤波的多目标轨迹运动轨迹预测(MMTP)算法。MMTP算法在目标检测阶段使用YOLOv4检测器提升目标检测的准确率和速度;在目标匹配阶段采用KM匹配算法将当前检测框的检测目标与上一时刻预测的预测框的目标进行数据关联,从而增强目标关联的准确性,避免目标遮挡、目标交错和漂移造成的目标丢失;在目标坐标预测阶段,提出改进卡尔曼滤波算法为每个运动目标预测下一帧位置坐标并画出预测框,提高非线性场景中目标坐标的预测精度,降低预测坐标的误差。使用MOT16与实际交通系统拍摄的视频序列数据集验证算法整体性能,仿真结果表明,MMTP在目标检测阶段具有较好的检测精度和速度,有效提升了算法整体的运行速度;在目标匹配阶段,MMTP算法能增强目标关联的准确性,减少目标丢失,比RMOT、POI、SORT、Deep-SORT和YVTP算法更优。     

基于密集轨迹和光流二值化图的行为识别算法

针对改进的密集轨迹算法（improved dense trajectories,iDT）提取的轨迹数量较为庞大的问题,提出了一种轨迹滤除方法。密集采样兴趣点,利用光流图计算每个兴趣点下一帧的位置进而组成轨迹,对每帧光流图进行最大值归一化以及二值化,得到光流二值化图,以此反映该点的运动是否相对显著。利用光流二值化图统计轨迹上各点的有效性从而判断轨迹是否满足有效条件,并将不满足条件的轨迹滤除,得到提纯的轨迹。为了验证算法的有效性,使用了行为识别领域的常用数据集KTH和UCF sports对算法进行验证,实验结果表明,该算法能在保证准确率的同时减少轨迹数量,并且计算量较小。     

多模式移动对象不确定性轨迹预测模型

以移动设备、车辆、飞机、飓风等移动对象不确定性轨迹预测问题为背景,将大规模移动对象数据作为研究对象,以频繁轨迹模式挖掘、高斯混合回归技术为主要研究手段,提出多模式移动对象轨迹预测模型,关键技术包括:1）针对单一运动模式,提出一种基于频繁轨迹模式树FTP-tree的轨迹预测方法,利用基于密度的热点区域挖掘算法将轨迹点划分成不同的聚簇,构建轨迹频繁模式树,挖掘频繁轨迹模式预测移动对象连续运动位置.不同数据集上实验结果表明基于FTP-tree的轨迹预测算法在保证时间效率的前提下预测准确性明显优于已有预测算法.2）针对复杂多模式运动行为,利用高斯混合回归方法建模,计算不同运动模式的概率分布,将轨迹数据划分为不同分量,利用高斯过程回归预测移动对象最可能运动轨迹.实验证明,相比于基于隐马尔科夫模型和卡尔曼滤波的预测方法,所提方法具有较高的预测准确性和较低的时间代价.     

Adaptive tracking system for a manoeuvring target using images with correlated noises

An effective scheme is proposed for estimating the state parameters of a manoeuvring target from a noisy image sequence, providing the sequence contains correlated noises and the trajectory of the target is disturbed by an unknown acceleration. For the correlated noises, a first-order difference operator is applied to the original image sequence to generate an artificial measurement sequence with only uncorrelated white noises. For the unknown acceleration, a Kalman filter augmented by a semi-Markov process and the bayesian theory is applied to form an adaptive filter. In the proposed tracking system, the filter first generates an artificial measurement at each sampling time from the observation. It then utilizes an artificial measurement sequence up to the current time instance to predict the a posteriori probability of each possible acceleration state. The weighted average of acceleration, where the weight is the a posteriori probability, is applied to derive the optimal estimates of the state parameters. Several computer simulations with external force applied at unknown times are performed to demonstrate the applicability and superiority of the proposed system.     

A switching models gain rotation algorithm for tracking a maneuvering target

Tracking of a target maneuvering in a 3D space is studied. Based on switching models describing the maneuver and/or nonmaneuver scenarios, the so-called switching models gain rotation algorithm (SMGRA) is presented. The proposed scheme incorporates a simple Kalman filter and a detector. Further it switches between the above two scenarios according to the detector's decision of target maneuverability. In both situations, the required gains of the algorithm are computed for uncoupled filters. A comparison study of the proposed scheme and several well-known filters is carried out for typical target trajectories in a naval gun fire control system. The tracking errors for the present algorithm are nearly identical to the extended Kalman filter (EKF), while the computation requirements are reduced by a factor of nine.