基于正则化观测矢量的H无穷粒子滤波红外目标跟踪方法

提出了一种新颖和鲁棒的红外图像序列中的目标跟踪方法。由于H无穷滤波器在系统噪声源不能确定或是未知的情况下具有较好的预测性能,所以以其估计得到的预测信息来分配粒子滤波算法的粒子。为解决粒子滤波的“采样枯竭”问题,正则化了H无穷粒子滤波器的观测矢量。同时,通过计算每个目标的亮度和局部标准差分布构成级联核的目标模型,以用于计算粒子集中各个粒子的加权值。对于目标的尺寸和表观信息变化的情况,以目标区域像素灰度值零阶矩的函数来调整跟踪窗口的大小,模型更新则通过更新目标模型的每个量化阶来实现。实验结果证明了所提出的红外图像目标跟踪方法是有效的,并且优于所比较的算法。     

基于人工蜂群算法的粒子滤波目标跟踪

针对基于粒子滤波的视频目标跟踪算法中由于粒子重采样过程而导致粒子贫化的问题,提出了一种基于人工蜂群算法的粒子滤波目标跟踪算法,利用群体智能的特点使得粒子集在重采样前得到优化,保持了粒子的多样性,从而解决了粒子贫化问题,同时增加了有效粒子的数目.实验结果表明,基于人工蜂群算法的粒子滤波跟踪算法,比标准粒子滤波跟踪算法所需粒子数更少,对目标遮挡、较复杂背景有较好的跟踪效果.     

Robust player detection and tracking in broadcast soccer video based on enhanced particle filter

It is significant to detect and track soccer players in broadcast sports video, which is helpful to analysis player activity and team tactics. However, it is challenging to efficiently detect and track soccer players with shots switched and noise caused by auditorium and billboards. And for multi-player tracking how to treat the increase or decrease of player are also difficult. In this paper, a robust player detection algorithm based on salient region detection and tracking based on enhanced particle filtering are proposed. Salient region detection is used to segment sports fields, and then soccer players are detected by edge detection combined with Otsu algorithm. For soccer players tracking, we use an enhanced particle filter which we improve the algorithm in sample and the likelihood function combing the color feature and edge feature. Experimental results show the proposed algorithm can quickly and accurately detect and track soccer players in broadcast video.     

Collaborative tracking via particle filter in wireless sensor networks

Target tracking is one of the main applications of wireless sensor networks. Optimized computation and energy dissipation are critical requirements to save the limited resource of the sensor nodes. A framework and analysis for collaborative tracking via particle filter are presented in this paper Collaborative tracking is implemented through sensor selection, and results of tracking are propagated among sensor nodes. In order to save communication resources, a new Gaussian sum particle filter, called Gaussian sum quasi particle filter, to perform the target tracking is presented, in which only mean and covariance of mixands need to be communicated. Based on the Gaussian sum quasi particle filter, a sensor selection criterion is proposed, which is computationally much simpler than other sensor selection criterions. Simulation results show that the proposed method works well for target tracking.     

基于改进粒子滤波的鲁棒目标跟踪算法

为了克服样本贫化现象导致的滤波发散,本文对重采样后的粒子进行有方向性的变异操作,在增加样本集的多样性同时使粒子集更集中均匀的分布在目标的邻域.同时把Mean Shift算法引入粒子滤波(PF)框架中,对PF估计结果迭代得到最优的目标状态,并用迭代得到的状态值控制粒子变异的方向.仿真实验表明,本文提出的方法具有更高的估计...     

Robust tracking algorithm for wireless sensor networks based on improved particle filter

Benefitting from its ability to estimate the target state's posterior probability density function (PDF) in complex nonlinear and non‐Gaussian circumstance, particle filter (PF) is widely used to solve the target tracking problem in wireless sensor networks. However, the traditional PF algorithm based on sequential importance sampling with re‐sampling will degenerate if the latest observation appear in the tail of the prior PDF or if the observation likelihood is too peaked in comparison with the prior. In this paper, we propose an improved particle filter which makes full use of the latest observation in constructing the proposal distribution. The quality prediction function is proposed to measure the quality of the particles, and only the high quality particles are selected and used to generate the coarse proposal distribution. Then, a centroid shift vector is calculated based on the coarse proposal distribution, which leads the particles move towards the optimal proposal distribution. Simulation results demonstrate the robustness of the proposed algorithm under the challenging background conditions. Copyright © 2010 John Wiley & Sons, Ltd.     

Robust model adaption for colour-based particle filter tracking with contextual information

Color-based particle filters have emerged as an appealing method for targets tracking. As the target may undergo rapid and significant appearance changes, the template (i.e. scale of the target, color distribution histogram) also needs to be updated. Traditional updates without learning contextual information may imply a high risk of distorting the model and losing the target. In this paper, a new algorithm utilizing the environmental information to update both the scale of the tracker and the reference appearance model for the purpose of object tracking in video sequences has been put forward. The proposal makes use of the well-established color-based particle filter tracking while differentiating the foreground and background particles according to their matching score. A roaming phenomenon that yields the estimation to shrink and diverge is investigated. The proposed solution is tested using both simulated and publicly available benchmark datasets where a comparison with six state-of-the-art trackers has been carried out. The results demonstrate the feasibility of the proposal and lie down foundations for further research on tackling complex visual tracking problems.     

Robust ambiguous target handling for visual object tracking

This paper presents a robust method of handling ambiguous targets (partial occlusion, split region or mixed state of the partial occlusion and the split region) for visual object tracking. The object model is a combination of bounding box features and expected object region. These object properties are very compact and allow us to track objects in a cluttered environment. The target state is classified in the first stage by a state classifier. The state classifier is defined from a weighted cross-correlation of normalized area and normalized distance which are defined from the comparison of background model- and the motion-based object detections. The correlation can categorize the target state by using the overlap quantity of the detected objects from the both object detections. If the target is merged state (partial occlusion), we will identify and track each object in the merged region by the bounding box features. If the targets are the split region, these regions are identified and grouped by the expected object region. If the target is the mixed state, we use the methods for handling the split and the merged region. Finally, experimental results show that the proposed method can deal with tracking in cluttered environments.     

一种新型多特征融合粒子滤波视觉跟踪算法

针对单一视觉信息在动态变化环境下描述目标不够充分、跟踪目标不够稳定的缺点,提出了一种基于粒子滤波框架的新型多特征融合的视觉跟踪算法。采用颜色和形状信息来描述运动模型,通过民主合成策略将两种信息融合在一起,使得跟踪算法能根据当前跟踪形势自适应调整两种信息的权重以期达到最佳的最大似然比,实现信息间的优势互补。在设计粒子滤波跟踪算法时,利用自适应信息融合策略构建似然模型,提高了粒子滤波跟踪算法在复杂场景下的稳健性。实验结果表明,多特征融合跟踪算法不仅能准确、高效地跟踪目标,而且对光照、姿态变化引起的目标表观变化具有良好的鲁棒性。     

基于粒子滤波的机载目标跟踪系统设计

基于微小型机载成像跟踪系统设计思想及需求,设计并实现了以高性能的DSP芯片TMS320-DM642为核心处理器,结合可编程逻辑器件CPLD和FPGA的实时图像跟踪处理平台。平台采用基于粒子滤波的目标跟踪算法,实现对目标的实时跟踪。采用卡尔曼滤波器,提高了粒子的利用效率,在改进了算法实时性的同时解决了图像跟踪系统的延时性问题,提高了跟踪系统的稳定性。算法仿真结果表明,与传统相关匹配算法相比,基于粒子滤波的跟踪算法具有更好的鲁棒性和实时性,能满足机载成像跟踪系统实时图像跟踪的要求。     

鲁棒的交互式多模粒子滤波跟踪器概率计算方法

针对传统交互式多模粒子滤波(IMMPF)跟踪器概率 计算方法在复杂环境下鲁棒性不足的缺陷,提出 一种新型的基于联合似然函数模型的子跟踪器概率计算方法。首先,计算基于跟踪结果与当 前外观 模型的巴氏距离作为瞬时似然函数,度量目标外观的剧烈变化;其次,利用l ₂范数规则化最小二乘算法构 建目标的重构外观模型,将其与跟踪结果的误差指数函数作为平稳似然函数,度量目标的缓 慢变化; 然后,基于加权求和策略得到跟踪器基于多种特征的联合似然函数;最后,将建立的联合似 然函数结 合上一帧的先验状态交互概率完成子跟踪器概率的更新。对复杂环境下跟踪器性能的在线评 估对比 结果验证了联合似然函数模型能有效评估跟踪器因不同干扰因素导致的性能变化,将其应用 于子跟踪器概率的计算能获得比主流算法更好的鲁棒性。     

Effective appearance model for probabilistic object tracking

This paper presents a robust object tracking approach via a spatially constrained colour model. Local image patches of the object and spatial relation between these patches are informative and stable during object tracking. So, we propose to partition an object into patches and develop a Spatially Constrained Colour Model (SCCM) by combining the colour distributions and spatial configuration of these patches. The likelihood of the candidate object is given by estimating the confidences of the pixels in the ...     

鲁棒的高斯和容积卡尔曼滤波红外目标跟踪算法

为提高恶劣测量环境下单站红外搜索与跟踪系统的跟踪性能,提出了一种鲁棒的高斯和容积卡尔曼滤波算法.首先,为改善滤波初值模糊问题,在容积卡尔曼滤波框架下将滤波器分为若干不同初值的子滤波器,利用似然函数逐步减小初值偏差较大的子滤波器权值;其次构建非线性程度判别量,在高非线性情况下将预测密度沿最大特征向量方向进行分割,提高滤波精度;最后利用等价权函数改善新息协方差,减小异常误差对滤波准确性和稳定性造成的影响.实验结果表明,不存在异常误差时,所提算法跟踪结果优于传统算法;存在异常误差时,传统滤波方法的精度明显降低,而所提算法依然能够得到准确可靠的跟踪结果.     

Asynchronous particle filter for tracking using non-synchronous sensor networks

This paper deals with the problem of tracking using a sensor network when the sensors are not synchronised. We propose a new algorithm called the asynchronous particle filter that, with much less computational burden than the traditional particle filter, has a slightly poorer performance. Thus, it is a good solution to real-time applications with non-synchronised sensors when high performance is required. The low computational burden of the method lies in the fact that we do not predict and update the state every time a measurement is collected. Its high performance is due to the fact that we account for the time instant at which each measurement was taken.     

Robust online multi-target visual tracking using a HISP filter with discriminative deep appearance learning

We propose a novel online multi-target visual tracker based on the recently developed Hypothesized and Independent Stochastic Population (HISP) filter. The HISP filter combines advantages of traditional tracking approaches like MHT and point-process-based approaches like PHD filter, and it has linear complexity while maintaining track identities. We apply this filter for tracking multiple targets in video sequences acquired under varying environmental conditions and targets density using a tracking-by-detection approach. We also adopt deep CNN appearance representation by training a verification-identification network (VerIdNet) on large-scale person re-identification data sets. We construct an augmented likelihood in a principled manner using this deep CNN appearance features and spatio-temporal information. Furthermore, we solve the problem of two or more targets having identical label considering the weight propagated with each confirmed hypothesis. Extensive experiments on MOT16 and MOT17 benchmark data sets show that our tracker significantly outperforms several state-of-the-art trackers in terms of tracking accuracy.     

采用混合t分布粒子滤波器的视觉跟踪

由于目标数量的变化,观测数据的岐义性和目标间的遮挡,多目标视觉跟踪问题面临多种困难.基于目标分布的有限t分布混合模型提出了一种混合t分布粒子滤波器以实现多目标跟踪.在算法中,每个被跟踪目标指派一个独立的粒子滤波器,显式处理当新目标出现在场景中时对应粒子滤波器的初始化,当被跟踪目标消失时,对应粒子滤波器的删除.混合t分布...     

紧耦合粒子滤波与均值漂移的红外目标跟踪

为了提高复杂背景下红外目标跟踪的准确性和鲁棒 性,提出了紧耦合粒子滤波(PF)与均值漂移(mean shift)的红外目标跟踪方法。在PF框 架下,利用一组5参数集(中心横坐标、中心纵坐标、宽度、高度以及倾斜角)作为状 态变量表 征随机的粒子样本;然后使用自适应均值漂移作为一种迭代模式寻找过程,对随机粒子样本 进行重新分配,使粒子向目标 状态的最大后验核密度估计方向移动,同时利用迭代过程中的Bhattacharyya系数对粒子的 权值进行更新;最后利用重新分配 后的加权粒子集合实现对红外目标的跟踪。实现结果表明,与一般的PF相比,本文方法能有 效减少所需粒子数(N=15),进而降 低跟踪耗时;与现有的PF与均值漂移相结合的方法相比,本文方法在耗费时间 仅增加14%的代价上,使跟踪误差大大降低(约 为原误差的1/3至1/4),准确性和鲁棒性得到显著提高;本文方法能够实现在复杂背景下稳 健准确地跟踪红外目标。     

A new multiple extended target tracking algorithm using PHD filter

A new multiple extended target tracking algorithm using the probability hypothesis density (PHD) filter is proposed in our study, to solve problems on tracking performance degradation of the extended target PHD (ET-PHD) filter under the nonlinear conditions and its intolerable computational requirement. It is noted that with the current Gaussian mixture implement of ET-PHD filter satisfying tracking performance could only be obtained under linear and Gaussian conditions. To extend the application of ET-PHD filter for nonlinear models, our study has derived a particle implement of ET-PHD (ET-P-PHD) filter. Our study finds that the main factors influencing the computational complexity of the ET-P-PHD filter are the partition number of measurement set and the calculation of non-negative coefficients of cells in partitions. With the pretreatment of measurements and application of a new K-means clustering based measurement set partition method, we have successfully decreased the partition number. In addition, a gating method for target state space, which is based on likelihood relationship between target state and measurement, is proposed to simplify the calculation of non-negative coefficients. Simulation results show that the algorithms proposed by our study could satisfyingly deal with multiple extended target tracking issues under nonlinear conditions, and lead to significantly lower computational complexity with tiny effect on tracking performance.     

基于无味粒子滤波的动态场景下高机动目标跟踪

针对传统粒子滤波的目标跟踪算法存在粒子退化问题,提出了基于无味粒子滤波(UPF)的目标跟踪算法。为了将当前观测信息融入,采用无味卡尔曼滤波(UKF)生成粒子滤波的提议分布,以改善滤波效果。针对目标在机动过程中引起的视觉形变以及背景的变化,又采用了颜色直方图作为目标的颜色分布模型,并与UPF相融合。仿真结果表明,该算法对动态场景下的高机动目标有较好的跟踪效果。