基于萤火虫优化粒子滤波的新型机动目标跟踪算法

交互式多模型粒子滤波算法需要多个模型才能对强机动目标进行跟踪,并且粒子滤波的重采样会导致粒子贫化现象,针对该问题提出一种新型机动目标跟踪方法.该方法首先将萤火虫群体的吸引和移动机制引入粒子滤波;再将改进粒子滤波引入交互式多模型中,通过智能寻优的方式提高交互式多模型的跟踪精度和稳定性.实验结果表明,相对于IMM-PF,改进方法可以用更少的时间达到同等精度,提高了机动目标跟踪的效率.     

Robust location tracking using a dual layer particle filter

Location awareness is an important part of many ubiquitous computing systems, but a perfect location system does not exist yet. Among many location tracking systems, we choose the radio frequency identification (RFID) system due to its various applications. However, the received signal strength indicator (RSSI) signals are too sensitive to the direction of the RFID reader’s antenna, the orientation of the RFID tag, human interference, and the diversity of propagation media that might be present. As a result, the direct use of a conventional particle filter does not provide satisfactory tracking performance. To overcome this problem, we suggest a dual layer particle filter, where the lower layer determines the tag’s location in the block level using a triangulation technique or the support vector machine (SVM) classifier, and the upper layer accurately estimates the tag’s location using the conventional particle filter within the pre-computed or classified block. This layered structure improves the location estimation and the tracking performance, because the location evidence from the lower layer effectively restricts the range of possible locations of the upper layer. We implement the proposed location tracking method using a ubiquitous RFID wireless network in an intelligent office, where several RFID readers are located in fixed locations and people or objects with active RFID tags move around the office. Extensive experiments show that the proposed location tracking method is so precise and robust that it is a good choice for person or object tracking in ubiquitous computing contexts. We also validate the usefulness of the proposed location tracking method by implementing it for a real-time people monitoring system in a noisy and complex steel mill.     

改进协方差矩阵的智能车视觉目标跟踪方法

智能车辆视觉目标具有非线性、噪声分布非高斯性的典型特点，现有算法难以实时估计目标的状态。针对识别物体复杂且多变，很难用完全的特征来描述待识别目标及其背景的不断变化，提出了一种用于融合颜色特征及SURF（Speed-Up Robust Features）特征的协方差矩阵来改进粒子滤波算法，从而提升视觉目标跟踪的实时性，满足智能车辆的要求。首先，对采集的图像进行预处理来获取感兴趣区域。接着，通过融合颜色特征及SURF特征构造范围感兴趣区域（Region Of Interest，ROI）的目标特征协方差矩阵，建立目标状态预测模型及状态观测模型，用于改进粒子滤波算法粒子重采样过程，实现对目标的精确跟踪。最后，将该方法与Mean-shift算法和颜色属性（CN）算法进行对比。实验结果表明，在智能车视觉跟踪过程中对光环境瞬时变化、目标物体存在短时遮挡以及目标物体姿态改变时，该算法在满足智能车辆对实时性要求的前提下，有效提升算法的精确度及鲁棒性。     

Tracking video objects with feature points based particle filtering

For intelligent video surveillance, the adaptive tracking of multiple moving objects is still an open issue. In this paper, a new multi-object tracking method based on video frames is proposed. A type of particle filtering combined with the SIFT (Scale Invariant Feature Transform) is proposed for motion tracking, where SIFT key points are treated as parts of particles to improve the sample distribution. Then, a queue chain method is adopted to record data associations among different objects, which could improve the detection accuracy and reduce the computational complexity. By actual road tests and comparisons, the system tracks multi-objects with better performance, e.g., real time implementation and robust against mutual occlusions, indicating that it is effective for intelligent video surveillance systems.     

卡尔曼粒子滤波的视频车辆跟踪算法研究

近年来,视频车辆跟踪作为城市智能交通系统（ITS）的一个关键技术受到关注。本文针对传统粒子滤波的非线性、非高斯性可能导致跟踪过程的不稳健性,提出一种基于卡尔曼粒子滤波的视频车辆跟踪算法,该算法利用基于重要区域的目标颜色直方图统计模型对视频车辆目标进行建模,并将其应用于卡尔曼滤波更新中,通过采用Mean Shift算法将卡尔曼滤波器引用到粒子滤波器当中,对车辆的运行轨迹进行校正,实现了局部线性滤波,实现了在保持跟踪系统整体上的非线性、非高斯性的同时,兼顾其局部的线性高斯特性。实验结果表明,本文所提出的方法与传统粒子滤波方法相比,能够更准确地对车辆进行跟踪,同时保证了在复杂环境下性能的稳健性。     

粒子滤波目标跟踪中的有效粒子数控制方法

针对视频目标跟踪中粒子滤波的粒子退化问题,提出一种有效粒子数控制方法。通过分析权值和有效粒子数对跟踪性能的影响,建立了有效粒子数控制的相关理论,并提出基于有效粒子数控制的粒子滤波目标跟踪算法。最后,建立了跟踪性能评价方法。大量的实验比较表明所提出的方法是有效的。     

EM-GMPF:一种基于EM的混合高斯粒子滤波器算法

粒子滤波器算法是一种基于贝叶斯推理和蒙特卡罗方法的非线性、非高斯动态系统的实时推理算法．因其具有灵活、易于实现、并行化等特点,成为统计学、信号处理、人工智能等领域新的研究热点,并被广泛地应用于目标跟踪等领域中．粒子滤波器算法中存在的主要问题是再取样步骤带来的粒子枯竭,从粒子滤波器的表示方法角度出发,提出了一种基于EM的混合高斯粒子滤波器算法,仿真数据和可视化跟踪实验表明,与传统的粒子滤波器算法和基于单高斯模型的粒子滤波器算法相比,该方法在降低对粒子数目需求的同时显著提高了粒子滤波器的估计性能．     

A Multiple Model Tracking Algorithm Based on an Adaptive Particle Filter

The interacting multiple model based on a particle filter fails to meet the requirements of real‐time performance when manoeuvring target tracking by radar due to deficiencies in its high calculation complexity. An improved particle filter based on landscape adaptive particle swarm optimization is proposed. This filter adopts the method of updating inertia weight, using not only local information and global information, but also preventing algorithm trapping in a local optimum, so the filter can find the optimal solution with less iteration. Additionally, an improved tracking model is presented. With the help of systematic resampling, the model can figure out the model index of particles. The experimental results prove that the new tracking algorithm not only improves manoeuvring target tracking accuracy, but also decreases computing complexity.     

多特征融合的自适应性粒子滤波跟踪算法

为提高粒子滤波视觉目标跟踪算法的实时性与鲁棒性,提出了一种基于多特征融合的自适应性粒子滤波跟踪算法。该算法利用颜色和结构特征表示目标,将两者融合于粒子滤波的框架中,利用融合后的信息计算粒子的权值,以降低算法受目标形变及复杂环境的影响。同时,根据跟踪预测的准确程度动态计算跟踪所需的粒子数目,对采样粒子集进行自适应调整,以提高粒子质量,降低粒子数量,减少算法运算时间。实验结果表明,所提算法对于每帧图像的平均计算时间相对于传统混合跟踪算法缩短了将近一半,而且算法的鲁棒性较强。     

Object tracking based on particle filter with discriminative features

This paper presents a particle filter-based visual tracking method with online feature selection mechanism. In color-based particle filter algorithm the weights of particles do not always represent the importance correctly, this may cause that the object tracking based on particle filter converge to a local region of the object. In our proposed visual tracking method, the Bhattacharyya distance and the local discrimination between the object and background are used to define the weights of the particles, which can solve the existing local convergence problem. Experiments demonstrates that the proposed method can work well not only in single object tracking processes but also in multiple similar objects tracking processes.     

基于二值掩码图像的粒子滤波目标跟踪快速算法

粒子滤波算法是进行运动目标跟踪的一种重要方法。针对传统粒子滤波算法在进行目标跟踪时存在的计算量大、实时性不足的问题,提出一种基于二值掩码图像的粒子滤波目标跟踪快速算法。该算法在传统粒子滤波算法的每个帧处理阶段产生二值掩码图像,再结合权重选择方法移除背景中权重较小的粒子,保留权重较大的重要粒子。提出的算法可以有效减少参与计算的粒子数目,节约算法的计算成本,从而提高目标跟踪的实时性。与传统粒子滤波算法进行比较,实验结果表明,提出的算法不仅能够有效地提高跟踪速度,而且跟踪结果的准确性和鲁棒性也有所增强。     

Multi-Camera Tracking with Adaptive Resource Allocation

Sensor fusion for object tracking is attractive since the integration of multiple sensors and/or algorithms with different characteristics can improve performance. However, there exist several critical limitations to sensor fusion techniques: (1) the measurement cost increases typically as many times as the number of sensors, (2) it is not straightforward to measure the confidence of each source and give it a proper weight for state estimation, and (3) there is no principled dynamic resource allocation algorithm for better performance and efficiency. We describe a method to fuse information from multiple sensors and estimate the current tracker state by using a mixture of sequential Bayesian filters (e.g., particle filter)—one filter for each sensor, where each filter makes a different level of contribution to estimate the combined posterior in a reliable manner. In this framework, multiple sensors interact to determine an appropriate sensor for each particle dynamically; each particle is allocated to only one of the sensors for measurement and a different number of particles is assigned to each sensor. The level of the contribution of each sensor changes dynamically based on its prior information and relative measurement confidence. We apply this technique to visual tracking with multiple cameras, and demonstrate its effectiveness through tracking results in videos.     

Vehicle tracking using a microwave radar for situation awareness

In this paper, a probabilistic target vehicle tracking method is proposed for situation awareness of intelligent cruise control (ICC) vehicle. The ICC vehicle considered herein is equipped with a 24 GHz microwave radar for tracking the preceding vehicle. To overcome the severe dispersion and noise of the microwave radar, a statistical model for the radar is built and it is applied to the hybrid particle filter. The hybrid particle filter is combined with the interacting multiple models (IMM) to track the preceding vehicle and predict the driver's intention. Furthermore, the modified hybrid particle filter is proposed to cope with the missing or multiple measurements of the microwave radar. Finally, a computer simulation is conducted and the validity of the proposed method is demonstrated.     

高速路的车道检测与车辆跟踪

基于智能交通的快速发展,研究了基于高速路的车道检测和车辆跟踪技术.对于多车道检测,根据路面与分道线灰度级相差较大的特点来实现车道路面的分割,接着结合直线方程和Catmull-Rom Spline插值算法来拟合分道线.对于单车道检测,首先基于HSV颜色空间和Sobel边缘提取方法对其进行有效分割,接着在透视变换空间中提取分道线坐标点并用二次多项式拟合分道线.针对车辆检测,使用Hog+Gentle-Adaboost分类算法实现无人车前方路面车辆的检测,接着基于车底阴影的特征对车底阴影进行检测以验证学习算法检测到的车辆区域的真伪性.针对车辆跟踪,采用动态二阶自回归模型的方法预测车辆的状态.其中,对于粒子滤波固有的粒子退化问题,引入Thompson_Taylor算法改善了粒子退化和低多样性的缺陷.本文的车道检测和车辆跟踪算法能较容易地移植在嵌入式平台,可靠性和准确性较高,且有助于进一步实现车道偏离报警和前向防撞系统.     

利用多特征混沌粒子滤波的视觉目标跟踪方法

随着摄像终端的增多以及自动视频分析需求量的增大,针对视频序列中存在突然运动、遮挡、运动模糊等干扰因素时传统视觉目标跟踪方法很难获得鲁棒性高、精确稳定的目标跟踪的问题,提出了利用多特征混沌粒子滤波的视觉目标跟踪方法。首先,基于非线性动力学预测进行混沌建模,利用混沌映射的梯度优化函数来搜索状态空间以找到参考轨迹;然后设计了一种用于视觉跟踪的混沌粒子滤波器,并改进运动表观模型,引入颜色、纹理和深度的特征完善滤波器的性能;最后,将多特征混沌粒子滤波器与其他视觉目标跟踪方法应用于VOT17和TB 数据集进行对比分析,以论证该方法的准确性。结果表明,提出的多特征混沌粒子滤波方法显著减少了粒子数量、搜索空间和滤波器发散,其精度高出其他方法约10%,在突然运动、遮挡和运动模糊等情况下整体性能优于其他几种对比方法。     

Tracking dynamic textures using a particle filter driven by intrinsic motion information

In this paper, a new method for tracking dynamic textures is presented. Its novelty is to use a particle filter driven by the intrinsic motion of the tracked dynamic texture. Many research works have indeed shown that dynamic textures are well characterized by their intrinsic motion (in proceedings of 4th international conference on computer recognition systems CORES’05, pp. 17–26, 2005). In this work, we compute motion statistics of dynamic textures and use them in the observation model of our particle filter. Our tracking method is successfully applied on test sequences. The algorithm is fast and is able to track a dynamic texture moving on another dynamic texture with different intrinsic dynamics. The method is also able to track a dynamic texture in cases where classical particle filters based on color information only fail. Comments and future prospects raised by this method are finally described.     

Dynamic appearance model for particle filter based visual tracking

This paper made major research on the target representation problem, which plays a significant role in visual tracking, but has received little attention in most researches. In order to fulfill the requirements of tracking robustness and effectiveness in practical conditions, a dynamic appearance model is constructed. Due to particle filter's excellent characteristics, it is employed in this paper not only to estimate target's state, but also to construct the dynamic observation model integrated by multiple cues. In the proposed method, a dynamic multi-cue integration model is constructed for particle filter framework. And a systematic study is done on evaluating cue's weight. Specially, a particle filter based weight tracker is designed to update multi-cue's integrating manner online, so as to adapt the observation model to target's appearance changes. In such a way, a double-particle-filter based tracking framework is formed, and it is field tested on a variety of videos in different tracking conditions. In the experiments and comparisons, the applicable conditions of the proposed dynamic model are discussed, and its robustness and effectiveness are demonstrated.     

Real-time visual tracking via multi-cue based adaptive particle filter framework

Visual tracking using particle filter has been extensively investigated due to its myriad of application in the field of computer vision. However, particle filter framework performance is heavily impaired due to its inherent problems namely, particle degeneracy and impoverishment. In addition, most of the tracking methods using single cue are greatly affected by dynamic environmental challenges. To address these issues, we propose an adaptive multi-cue particle filter based real-time visual tracking framework. Three complementary cues namely, color histogram, LBP and pyramid of histogram of gradient have been exploited for object’s appearance model. These cues are integrated using the proposed adaptive fusion model for the automatic boosting of important particles and suppression of unimportant particles. Resampling method using butterfly search optimization relocate low performing particles to high likelihood area. Proposed outlier detection mechanism not only helps in detecting low performing particles but also aids in updating of the reference dictionary. Online estimation of cue reliability along with its multi-cue fusion leads to quick adaptation of the proposed tracker. On average of the outcome, our tracker achieves average center location error of 6.89 (in pixels) and average F-measure of 0.786 when evaluated on OTB-100 and VOT dataset against 13 others state-of-the-art.

     

视线追踪系统中眼睛跟踪方法研究

为解决视线追踪系统中红外图像瞳孔跟踪鲁棒性差的问题, 提出一种基于伪彩色图的粒子滤波瞳孔跟踪算法. 利用亮暗瞳现象, 提出三通道伪彩色图(Triple-channel pseudo-color map, TCPCM)的概念, 并将其引入瞳孔跟踪过程. TCPCM充分利用了各通道信息, 瞳孔区域的色彩明显与人脸其他部位不同, 提高了跟踪的稳定性与精确性. 采用了直方图相似性度量与几何相似性度量相结合的二次更新的瞳孔感知模型, 提高了粒子权重的可信性. 针对实时性要求, 引入快速特征提取步骤, 减少特征提取的时间, 提高特征提取的可靠性. 实验结果表明, 该算法在瞳孔目标检测效果、跟踪稳定性和运行时间方面都有良好的性能.     

基于分块颜色直方图和粒子滤波的物体跟踪

在使用粒子滤波的跟踪方法中,颜色直方图经常被用来作为目标特征。但是普通的颜色直方图易受与跟踪物颜色相似的背景和其他物体的干扰,并且在跟踪目标被部分遮挡后性能也将下降。为解决这些问题,受hog特征启发,提出一种分块重叠的颜色直方图,并且根据分块直方图特点,重新设计了粒子滤波系统的权重计算方法和模型更新方法。实验证明该系统优于传统的颜色直方图特征。