Fast and robust object tracking via Accept–Reject color histogram-based method

We present a new framework for real-time tracking method of complex non-rigid objects. This new method successfully coped with camera motion, partial occlusions, and target scale variations. The shape of the object tracker is approximated by an ellipse and its appearance by histogram based features derived from local image properties. We use an efficient search scheme (Accept–Reject color histogram-based method (AR), using Bhattacharyya kernel as a similarity measure) to find the image region with a histogram most similar to the target of object tracker. In this paper, we address the problem of scale/shape adaptation and orientation changes of the target. The proposed approach is compared with recent state-of-the-art algorithms. Extensive experiments are performed to testify the proposed method and validate its robustness and effectiveness to track the scale and orientation changes of the target in real-time.     

Mean LBP and modified fuzzy C-means weighted hybrid feature for illumination invariant mean-shift tracking

Object tracking is a critical task in surveillance and activity analysis. Two main issues for tracking are appearance (illumination) and structural (size of a target) variations of the object. We propose a method which is robust and addresses these issues by incorporating features that are less variant to these changes. The proposed features are mean local binary pattern (mLBP), an illumination invariant texture feature, and modified fuzzy c-means (MFCM) weighted color histogram to handle both illumination and scale changes. These features are combined to form a hybrid mean-shift (MS) vector and used in the MS vector framework for target tracking. Experimental results using standard benchmark videos show that the proposed scheme can lead to better localization and robust tracking in challenging illumination scenarios, when compared to several existing tracking algorithms.     

A Robust Particle Filter-Based Method for Tracking Single Visual Object Through Complex Scenes Using Dynamical Object Shape and Appearance Similarity

This paper addresses the issue of tracking a single visual object through crowded scenarios, where a target object may be intersected or partially occluded by other objects for a long duration, experience severe deformation and pose changes, and different motion speed in cluttered background. A robust visual object tracking scheme is proposed that exploits the dynamics of object shape and appearance similarity. The method uses a particle filter where a multi-mode anisotropic mean shift is embedded to improve the initial particles. Comparing with the conventional particle filter and mean shift-based tracking (Shan et al. 2004), our method offers the following novelties: We employ a fully tunable rectangular bounding box described by five parameters (2D central location, width, height, and orientation) and full functionaries in the joint tracking scheme; We derive the equations for the multi-mode version of the anisotropic mean shift where the rectangular bounding box is partitioned into concentric areas, allowing better tracking objects with multiple modes. The bounding box parameters are then computed by using eigen-decomposition of mean shift estimates and weighted averaging. This enables a more efficient re-distributions of initial particles towards locations associated with large weights, hence an efficient particle filter tracking using a very small number of particles (N = 15 is used). Experiments have been conducted on video containing a range of complex scenarios, where tracking results are further evaluated by using two objective criteria and compared with two existing tracking methods. Our results have shown that the propose method is robust in terms of tracking drift, tightness and accuracy of tracked bounding boxes, especially in scenarios where the target object contains long-term partial occlusions, intersections, severe deformation, pose changes, or cluttered background with similar color distributions.     

基于颜色属性直方图的尺度目标跟踪算法研究

利用目标颜色信息的跟踪算法,容易受到环境光照、尺度变化、相似背景等因素的干扰,导致跟踪任务失败。为了克服以上问题,该文提出一种基于颜色属性空间的鲁棒尺度目标跟踪算法。该算法首先将原始的RGB颜色空间映射到颜色属性(Color Names, CN)空间,减少目标颜色在跟踪过程中受环境变化影响。然后采用一种背景加权约束的颜色属性直方图,来抑制相似背景的干扰。最后,为了解决目标尺度变化带来的影响,先用梯度上升法粗略估计尺度,再用约束项精确求解尺度,并利用反向一致性检验,进一步提高尺度估计的准确性。该文选取了5段典型视频进行实验,并与相关算法进行比较。结果表明所提算法能够消除环境光照、阴影、相似背景和尺度变化等因素所带来的影响,在中心位置误差和跟踪成功率性能指标上,优于其它算法。     

基于ICA分布模型的粒子滤波跟踪算法

为了在复杂背景、部分遮挡和光照变化等因素干扰的情况下鲁棒地跟踪视频序列中感兴趣的运动目标,提出了一种改进的粒子滤波跟踪算法。该算法针对颜色信息在目标表述中存在的不足,首先对观测模型进行改进,提出了一种基于ICA特征分布的目标模型,将基于核函数的目标特征描述转换到ICA特征空间,由于光照变化引起灰度变化经ICA后仍是同一分量,因此能有效的适应光照变化,不仅考虑并充分利用了空间信息。有效的解决了光照变化及背景颜色相近造成的目标丢失现象,提高了目标跟踪算法的鲁棒性。同时,考虑到粒子的退化现象,将均值平移算法嵌入到粒子滤波的跟踪框架中,待各粒子经过系统传播后,利用均值平移算法使粒子向其领域局部极大值处移动,使得粒子集中在测量模型的局部区域内,只需少量的粒子就覆盖了尽可能的目标分布,很好地克服了粒子滤波器的退化现象并有效缩短了计算时间,提高了目标跟踪算法的准确性和系统的实时性。实验表明,该算法不仅能在复杂背景下准确的跟踪目标,而且在光线变化和部分遮挡情况下也能保证不丢失目标。      

基于局部相似的红外小目标跟踪算法

连续图片序列中的运动对象与其局部背景保持相对稳定。相邻图像序列的目标局部灰度分布具有相似性。针对红外小目标跟踪杂波干扰与运动模糊问题,设计了基于局部相似的目标增强方法,并提出一种基于局部相似和运动估计的跟踪算法。该算法根据目标的局部相似性构建搜索空间,采用运动估计机制缩小搜索域,然后利用时空上下文学习跟踪算法产生跟踪结果。实验表明,本文方法能够有效应对杂波干扰和运动模糊情况,实现红外小目标的稳健跟踪,且具有较好的实时性。     

Robust Visual Tracking with SIFT Features and Fragments Based on Particle Swarm Optimization

We propose a novel approach for visual tracking based on a particle swarm optimization (PSO) framework using SIFT feature points correspondence and multiple fragments in a candidate target region to cope with the problems of partial occlusions, illumination changes, and large motion changes of the tracked target. Firstly, optimal search in the successive frame tracking process is performed by the PSO algorithm, which guides all particles towards the global optima state based on a fitness function. Then, the SIFT feature information is integrated into the iterative results of PSO to acquire a more accurate tracking state. Secondly, we present an effective appearance model updating criterion, which evaluates which fragments in appearance model need updating at each frame. However, the fragments with occluded parts or low quality measure values are not updated. The method for updating appearance model is introduced to improve the tracking performance. Compared with state-of-the-art algorithms, the proposed method can still stably track the target during the course of long-term partial occlusions using superior fragments of tracked target. The experiment results demonstrate the effectiveness of our algorithm in complex environments where the target object undergoes partial occlusions and large changes in pose and illumination.     

基于改进加权增强局部对比度测量的红外小目标检测方法

红外弱小目标检测技术是红外搜索与跟踪系统的重要组成部分(IRST)。一般来说,在复杂背景环境下,红外弱小目标检测往往会有高虚警率和低检测率的问题。为了解决这一问题,提出一个改进的加权增强局部对比度测量（IWELCM）检测框架,具有重要意义。首先,通过将局部对比度机制与信杂比(SCR)的计算相结合,提出一个增强的局部对比度测量方法,在增强图像中疑似红外弱小目标区域的同时也提高图像的SCR。其次,通过利用红外图像中弱小目标的特性,以及目标与周围背景的统计差异,提出一个改进的加权函数来进一步增强目标和抑制背景。最后,采用一个自适应阈值分割的方法去获取检测的目标。在不同场景的数据集上的对比实验表明,与七种现有流行的方法相比,提出方法在复杂背景下能够有效地从干扰对象中提取真实的红外弱小目标,具有更好的检测性能。     

基于增强群跟踪器和深度学习的目标跟踪

为解决基于外观模型和传统机器学习目标跟踪易出现目标漂移甚至跟踪失败的问题,该文提出以跟踪-学习-检测(TLD)算法为框架,基于增强群跟踪器(FoT)和深度学习的目标跟踪算法。FoT实现目标的预测与跟踪,增添基于时空上下文级联预测器提高预测局部跟踪器的成功率,快速随机采样一致性算法评估全局运动模型,提高目标跟踪的精确度。深度去噪自编码器和支持向量机分类器构建深度检测器,结合全局多尺度扫描窗口搜索策略检测可能的目标。加权P-N学习对样本加权处理,提高分类器的分类精确度。与其它跟踪算法相比较,在复杂环境下,不同图片序列实验结果表明,该算法在遮挡、相似背景等条件下具有更高的准确度和鲁棒性。     

基于稀疏表示多子模板的鲁棒目标跟踪算法

目标跟踪技术是一项富有挑战性的研究课题,在红外成像搜索、红外精确制导、智能监控、运动识别等领域有着广泛的应用。文中提出了一种基于稀疏表示多子模板的鲁棒目标跟踪算法。首先,提出一种基于自适应辨别信息的子模板选择方法,最大限度地捕捉目标的结构信息,提高模板子块的整体描述;针对直方图对光照敏感的缺点,引入了稀疏表示理论对子模板进行描述,提高模板子块的表达能力和适应能力;其次通过构造表决图的形式对目标位置进行表决和融合决策;最后设计了一种动态的子模板更新策略,来有效地应对目标外观模型的变化。在大量测试图像序列中的仿真实验表明,文中所提算法可以有效应对形变、光照变化、部分遮挡、完全遮挡以及虚假目标干扰和背景干扰,具有较高的鲁棒性。     

基于局部分块和模型更新的视觉跟踪算法

针对目标跟踪过程中的目标表观变化、背景干扰及发生遮挡等问题,该文提出一种基于局部分块和模型更新的视觉跟踪算法。该文采用粗搜索与精搜索相结合的双层搜索方法来提高目标的定位精度。首先,在包含部分背景区域的初始跟踪区域内构建目标模型。然后,利用基于积分直方图的局部穷搜索算法初步确定目标的位置,接着在当前跟踪区域内通过分块学习来精确搜索目标的最终位置。最后,利用创建的模型更新域对目标模型进行更新。该文主要针对分块跟踪中的背景抑制、模型更新等方面进行了研究,实验结果表明该算法对目标表观变化、背景干扰及遮挡情况的处理能力都有所增强。     

复杂背景下基于加权信息熵的目标初始位置修正算法

对于某些目标跟踪算法而言,初始位置的选取是否精确是算法能否有效跟踪的关键点之一。从目标的灰度信息分布特点出发,提出一种基于加权信息熵的初始位置修正算法,首先,在搜索窗口中获取测试样本,然后,计算各个样本的加权信息熵,接着,通过先验信息对样本进行筛选,获得熵值最小区域,从而得到修正后的目标位置。从背景和目标的可区分性上来验证算法的有效性,实验结果表明,对于处在复杂背景下的目标,该算法能正确且可靠稳定地对其位置进行修正。     

基于空间可靠性约束的鲁棒视觉跟踪算法

针对复杂背景下目标容易发生漂移的问题,该文提出一种基于空间可靠性约束的目标跟踪算法。首先通过预训练卷积神经网络(CNN)模型提取目标的多层深度特征,并在各层上分别训练相关滤波器,然后对得到的响应图进行加权融合。接着通过高层特征图提取目标的可靠性区域信息,得到一个二值注意力矩阵,最后将得到的二值矩阵用于约束融合后响应图的搜索范围,范围内的最大响应值即为目标的中心位置。为了处理长时遮挡问题,该文提出一种基于首帧模板信息的随机选择更新策略。实验结果表明,该算法在应对相似背景干扰、遮挡、超出视野等多种场景均有良好的性能表现。     

基于多特征相关滤波的红外目标跟踪

为实现在复杂背景和多干扰条件下红外目标的稳 定跟踪,提出一种基于多特征相关滤波的红外目标 跟踪算法。首先综合考虑生物视觉关注特性及目标运动特性,提取目标区域的空间特征和 运动特征,进而融合一种改进的卷积特征,生成多特征权值函数;然后在传统 相关滤波的基础上,引入多特 征权值函数用以表征不同候选区域的重要程度,形成权值相关滤波的红外目标跟踪框架;最 终得到能够表 征目标位置的置信图,从而完成红外目标的鲁棒跟踪。在6组不同条件下红外视频序列上的 实验结果表明, 和经典目标跟踪算法相比,本文方法在复杂背景下的平均跟踪成功率提升15%左右,能够有 效应对相似虚 假目标、遮挡、背景辐射强度变化和探测器晃动等不良因素的影响,适用于复杂背景条件下 的红外目标跟踪。     

复杂场景下的加权粒子滤波行人跟踪方法

针对粒子滤波跟踪算法在行人目标遮挡、光线干扰以及背景与行人相似等情形下,目标易发生漂移、跟踪精度不高的问题,本文提出一种加权粒子滤波行人跟踪方法。该方法联合遮挡模型和Online Boosting算法,利用在线学习实时更新强分类器,并结合跟踪时建立的遮挡模型,以及行人运动时与上一次目标位置的距离、相似度等影响因子,对粒子权重进行重新构造,实现了复杂变化场景下的行人自适应跟踪。通过对PETS-L2S1公共数据集和自有数据集分别进行实验,可以得到本文提出的方法能有效去除目标遮挡、相似背景以及光线突变的干扰,实现稳定、准确、实时的行人跟踪。      

改进的Camshift与Kalman融合的目标跟踪算法

Camshift算法主要利用物体的颜色信息进行跟踪,在复杂背景条件下容易造成目标的跟丢,且在目标被遮挡时,也容易造成跟踪失效。本文提出了一种改进的Camshift目标跟踪算法。首先将目标图像的HSV模型的三个分量进行加权建立一种新的目标颜色模型,然后由对整帧图像计算反向投影改为比搜索窗口稍大的区域计算反向投影,减少了相似背景的干扰。同时为了解决遮挡问题,结合了Kalman滤波器,有效地预测了目标的位置。实验表明,本算法能够避免背景颜色干扰和解决遮挡问题,实现了对运动目标准确跟踪。     

小型无人机对地目标盘旋跟踪仿真

主要研究了小型无人机对地跟踪问题。针对小型无人机的盘旋状态,为了提高该情况下的目标形变,光影变化及遮挡等干扰的鲁棒性与实时性,采用基于抑制背景的MeanShift目标跟踪算法,即对目标周围和背景差异显著性较大的区域赋予较大的权值,从而把背景对目标的影响有效抑制,实现目标始终在跟踪方框内。通过架构模拟实验,该方案能够有效减小目标的连续形变以及光照变化对目标的影响;对于目标旁边景物的遮挡也有较强的抗干扰能力,而且该方案可靠性高、算法复杂度低、实时性较强。     

基于空时线索的TLD视频跟踪算法

视频跟踪是计算机视觉领域的研究热点之一,Tracking-Learning-Detection(TLD)是近年来提出的一种有效的视频跟踪框架。针对短时遮挡以及复杂可变背景环境下的目标跟踪问题,提出了一种基于空时线索的TLD视频跟踪算法。在该算法中,采用由局部图像块的多通道特征训练生成的霍夫森林进行检测,通过多个局部图像块引入目标相关的空间位置信息,提高了算法的区分能力;然后,根据图像块对光流跟踪初始位置进行随机化布置并利用空间位置信息对光流跟踪结果进行加权,改善光流跟踪的性能;最后,对光流跟踪输出置信度与霍夫森林检测输出置信度进行自适应空时融合,综合提高目标的跟踪精度。实验结果表明,与原始TLD算法相比,本文算法能够更有效地处理遮挡问题,实现复杂背景环境下的鲁棒目标跟踪。      

Spatio-temporal crowd density model in a human detection and tracking framework

Recently significant progress has been made in the field of person detection and tracking. However, crowded scenes remain particularly challenging and can deeply affect the results due to overlapping detections and dynamic occlusions. In this paper, we present a method to enhance human detection and tracking in crowded scenes. It is based on introducing additional information about crowds and integrating it into the state-of-the-art detector. This additional information cue consists of modeling time-varying dynamics of the crowd density using local features as an observation of a probabilistic function. It also involves a feature tracking step which allows excluding feature points attached to the background. This process is favorable for the later density estimation since the influence of features irrelevant to the underlying crowd density is removed. Our proposed approach applies a scene-adaptive dynamic parametrization using this crowd density measure. It also includes a self-adaptive learning of the human aspect ratio and perceived height in order to reduce false positive detections. The resulting improved detections are subsequently used to boost the efficiency of the tracking in a tracking-by-detection framework. Our proposed approach for person detection is evaluated on videos from different datasets, and the results demonstrate the advantages of incorporating crowd density and geometrical constraints into the detection process. Also, its impact on tracking results have been experimentally validated showing good results.     

基于在线判别式字典学习的鲁棒视觉跟踪

现有子空间跟踪方法较好地解决了目标表观变化和遮挡问题,但是它对复杂背景下目标跟踪的鲁棒性较差。针对此问题,该文首先提出一种基于Fisher准则的在线判别式字典学习模型,利用块坐标下降和替换操作设计了该模型的在线学习算法用于视觉跟踪模板更新。其次,定义候选目标编码系数与目标样本编码系数均值之间的距离为系数误差,提出以候选目标的重构误差与系数误差的组合作为粒子滤波的观测似然跟踪目标。实验结果表明:与现有跟踪方法相比,该文跟踪方法具有较强的鲁棒性和较高的跟踪精度。