共查询到20条相似文献,搜索用时 31 毫秒
1.
An important problem in tracking methods is how to manage the changes in object appearance, such as illumination changes, partial/full occlusion, scale, and pose variation during the tracking process. In this paper, we propose an occlusion free object tracking method together with a simple adaptive appearance model. The proposed appearance model which is updated at the end of each time step includes three components: the first component consists of a fixed template of target object, the second component shows rapid changes in object appearance, and the third one maintains slow changes generated along the object path. The proposed tracking method not only can detect occlusion and handle it, but also it is robust against changes in the object appearance model. It is based on particle filter which is a robust technique in tracking and handles non-linear and non-Gaussian problems. We have also employed a meta-heuristic approach that is called Modified Galaxy based Search Algorithm (MGbSA), to reinforce finding the optimum state in the particle filter state space. The proposed method was applied to some benchmark videos and its results were satisfactory and better than results of related works. 相似文献
2.
This paper presents a visual object tracking system which is tolerant to external imaging factors such as illumination, scale, rotation, occlusion and background changes. Specifically, an integration of an online version of total-error-rate minimization based projection network with an observation model of particle filter is proposed to effectively distinguish between the target object and the background. A re-weighting technique is proposed to stabilize the sampling of particle filter for stochastic propagation. For self-adaptation, an automatic updating scheme and extraction of training samples are proposed to adjust to system changes online. Our qualitative and quantitative experiments on 16 public video sequences show convincing performances in terms of tracking accuracy and computational efficiency over competing state-of-the-art algorithms. 相似文献
3.
Aiming at tracking visual objects under harsh conditions, such as partial occlusions, illumination changes, and appearance variations, this paper proposes an iterative particle filter incorporated with an adaptive region-wise linear subspace (RWLS) representation of objects. The iterative particle filter employs a coarse-to-fine scheme to decisively generate particles that convey better hypothetic estimates of tracking parameters. As a result, a higher tracking accuracy can be achieved by aggregating the good hypothetic estimates from particles. Accompanying with the iterative particle filter, the RWLS representation is a special design to tackle the partial occlusion problem which often causes tracking failure. Moreover, the RWLS representation is made adaptive by exploiting an efficient incremental updating mechanism. This incremental updating mechanism can adapt the RWLS to gradual changes in object appearances and illumination conditions. Additionally, we also propose the adaptive mechanism to continuously adjust the object templates so that the varying appearances of tracked objects can be well handled. Experimental results demonstrate that the proposed approach achieves better performance than other related prior arts. 相似文献
4.
5.
6.
目的 在目标跟踪中,基本粒子滤波算法和传统模型更新策略在遮挡、光照变化以及自身旋转等情况下通常存在鲁棒性较差的问题。为解决该问题,提出一种基于选择性模型不定时更新的粒子滤波视觉跟踪算法。方法 本文算法在粒子滤波框架下,定时检测目标变化,采用最速梯度下降法判断更新时机。仅当目标本身逐渐姿态改变而不受背景干扰时更新目标模型;在发生遮挡或光照改变较大时则不更新,保持当前模型继续跟踪。结果 通过对示例视频的计算机视觉跟踪,在跟踪目标中心位置误差、覆盖率、精度及成功率比较上,本文算法均能表现出较优的实验结果,这表明:本文算法能够对目标模型进行选择性更新,与对比算法比较,总体能够更加有效解决视频中遮挡、光照变化以及自身旋转等情况下的鲁棒跟踪问题。结论 由于本文算法未考虑尺度,在尺度不变的多种复杂变化条件下,本文算法均具有较好的鲁棒性。 相似文献
7.
基于粒子滤波与稀疏表达的目标跟踪方法 总被引:1,自引:0,他引:1
针对视频序列图像目标跟踪中的光照变化问题,提出一种在粒子滤波器框架内,基于目标的局部二元模式(LBP)纹理特征,使用稀疏表达进行目标跟踪的方法。当前帧的跟踪粒子由前一帧的跟踪结果按高斯分布来生成。通过解l1正则化最小二乘方问题,获得每个粒子对应于模板子空间的稀疏表达,确定当前帧图像中的跟踪目标。然后使用粒子滤波器生成下一帧跟踪的粒子分布。在跟踪过程中采用新的动态模板更新策略更新模板空间中的模板。实验结果证明该方法的有效性和先进性。 相似文献
8.
目的 虽然基于稀疏表示的目标跟踪方法表现出了良好的跟踪效果,但仍然无法彻底解决噪声、旋转、遮挡、运动模糊、光照和姿态变化等复杂背景下的目标跟踪问题。针对遮挡、旋转、姿态变化和运动模糊问题,提出一种在粒子滤波框架内,基于稀疏表示和先验概率相结合的目标跟踪方法。方法 通过先验概率衡量目标模板的重要性,并将其引入到正则化模型中,作为模板更新的主要依据,从而获得一种新的候选目标稀疏表示模型。结果 在多个测试视频序列上,与多种流行算法相比,该算法可以达到更好的跟踪性能。在5个经典测试视频下的平均中心误差为6.77像素,平均跟踪成功率为97%,均优于其他算法。结论 实验结果表明,在各种含有遮挡、旋转、姿态变化和运动模糊的视频中,该算法可以稳定可靠地跟踪目标,适用于视频监控复杂场景下的目标跟踪。 相似文献
9.
Jesús Martínez-del-Rincón Carlos Orrite Carlos Medrano 《Pattern recognition letters》2011,32(2):210-220
Colour-based particle filters have been used exhaustively in the literature, given rise to multiple applications. However, tracking coloured objects through time has an important drawback, since the way in which the camera perceives the colour of the object can change. Simple updates are often used to address this problem, which imply a risk of distorting the model and losing the target. In this paper, a joint image characteristic-space tracking is proposed, which updates the model simultaneously to the object location. In order to avoid the curse of dimensionality, a Rao-Blackwellised particle filter has been used. Using this technique, the hypotheses are evaluated depending on the difference between the model and the current target appearance during the updating stage. Convincing results have been obtained in sequences under both sudden and gradual illumination condition changes. 相似文献
10.
Chengjun Xie Jieqing Tan Peng Chen Jie Zhang Lei He 《Machine Vision and Applications》2014,25(7):1859-1876
When objects undergo large pose change, illumination variation or partial occlusion, most existing visual tracking algorithms tend to drift away from targets and even fail to track them. To address the issue, in this paper we propose a multi-scale patch-based appearance model with sparse representation and provide an efficient scheme involving the collaboration between multi-scale patches encoded by sparse coefficients. The key idea of our method is to model the appearance of an object by different scale patches, which are represented by sparse coefficients with different scale dictionaries. The model exploits both partial and spatial information of targets based on multi-scale patches. Afterwards, a similarity score of one candidate target is input into a particle filter framework to estimate the target state sequentially over time in visual tracking. Additionally, to decrease the visual drift caused by frequently updating model, we present a novel two-step object tracking method which exploits both the ground truth information of the target labeled in the first frame and the target obtained online with the multi-scale patch information. Experiments on some publicly available benchmarks of video sequences showed that the similarity involving complementary information can locate targets more accurately and the proposed tracker is more robust and effective than others. 相似文献
11.
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。 相似文献
12.
13.
Zhongpei Wang Hao Wang Jieqing Tan Peng Chen Chengjun Xie 《Multimedia Tools and Applications》2017,76(10):12181-12203
There are many visual tracking algorithms that are based on sparse representation appearance model. Most of them are modeled by local patches with fixed patch scale, which make trackers less effective when objects undergone appearance changes such as illumination variation, pose change or partial occlusion. To solve the problem, a novel appearance representation model is proposed via multi-scale patch based sparse coding histogram for robust visual tracking. In this paper, the appearance of an object is modeled by different scale patches, which are represented by sparse coding histogram with different scale dictionaries. Then a similarity measure is applied to the calculation of the distance between the sparse coding histograms of target candidate and target template. Finally, the similarity score of the target candidate is passed to a particle filter to estimate the target state sequentially in the tracking process. Additionally, in order to decrease the visual drift caused by partial occlusion, an occlusion handling strategy is adopted, which takes the spatial information of multi-scale patches and occlusion into account. Based on the experimental results on some benchmarks of video sequences, our tracker outperforms state-of-the-art tracking methods. 相似文献
14.
Color-based visual object tracking is one of the most commonly used tracking methods. Among many tracking methods, the mean shift tracker is used most often because it is simple to implement and consumes less computational time. However, mean shift trackers exhibit several limitations when used for long-term tracking. In challenging conditions that include occlusions, pose variations, scale changes, and illumination changes, the mean shift tracker does not work well. In this paper, an improved tracking algorithm based on a mean shift tracker is proposed to overcome the weaknesses of existing methods based on mean shift tracker. The main contributions of this paper are to integrate mean shift tracker with an online learning-based detector and to newly define the Kalman filter-based validation region for reducing computational burden of the detector. We combine the mean shift tracker with the online learning-based detector, and integrate the Kalman filter to develop a novel tracking algorithm. The proposed algorithm can reinitialize the target when it converges to a local minima and it can cope with scale changes, occlusions and appearance changes by using the online learning-based detector. It updates the target model for the tracker in order to ensure long-term tracking. Moreover, the validation region obtained by using the Kalman filter and the Mahalanobis distance is used in order to operate detector in real-time. Through a comparison against various mean shift tracker-based methods and other state-of-the-art methods on eight challenging video sequences, we demonstrate that the proposed algorithm is efficient and superior in terms of accuracy and speed. Hence, it is expected that the proposed method can be applied to various applications which need to detect and track an object in real-time. 相似文献
15.
16.
针对传统的或概率性的协方差跟踪中采用的矩形区域协方差描述子不能精确反映目标的非刚性形变的问题,提出椭圆区域协方差描述子,将各种不同类型的特征融入统一的模型中,实现了基于多特征的目标跟踪,并将其与卡尔曼粒子滤波相结合,提出一种鲁棒的视觉目标跟踪方法.实验结果表明,该方法对光线变化、噪声干扰、部分和完全遮挡、目标的尺度和角度的变化以及目标的快速运动都具有较好的鲁棒性. 相似文献
17.
W. Ketchantang S. Derrode L. Martin S. Bourennane 《Machine Vision and Applications》2008,19(5-6):457-466
To track objects in video sequences, many studies have been done to characterize the target with respect to its color distribution. Most often, the Gaussian mixture model (GMM) is used to represent the object color density. In this paper, we propose to extend the normality assumption to more general families of distributions issued from the Pearson’s system. Precisely, we propose a method called Pearson mixture model (PMM), used in conjunction with Gaussian copula, which is dynamically updated to adapt itself to the appearance change of the object during the sequence. This model is combined with Kalman filtering to predict the position of the object in the next frame. Experimental results on gray-level and color video sequences show tracking improvements compared to classical GMM. Especially, the PMM seems robust to illumination variations, pose and scale changes, and also to partial occlusions, but its computing time is higher than the computing time of GMM. 相似文献
18.
为了解决机器人在未知环境下的目标跟踪问题,提出了一种基于粒子滤波的机器人同时定位、地图构建与目标跟踪方法.该方法采用Rao-Blackwellized粒子滤波器对机器人位姿状态、标志柱分布和目标位置同时进行估计.该方法中,粒子群的总体分布情况表征机器人位姿状态,而每个粒子均包含2类EKF滤波器,其中一类用来完成对标志柱分布的估计,另一类用来完成对目标状态的估计,粒子的权值则由粒子状态相对于标志柱和目标状态2类相似度共同产生.通过仿真和实体机器人实验验证了该方法的有效性. 相似文献
19.
首先, 根据目标运动与姿态角的关系, 分析目标在偏航角和俯仰角下的速度变化, 进而推导出姿态角辅助三维目标跟踪模型; 然后, 针对姿态角量测非高斯情况, 在分析均方根容积卡尔曼滤波的基础上, 提出新的高斯和均方根容积卡尔曼滤波算法, 以提高非线性非高斯的处理能力; 最后, 结合不同运动模式下姿态角分量的特点, 建立姿态角分量不同的跟踪模型, 通过模型切换实现对姿态角机动的跟踪. 仿真结果验证了所提出跟踪模型和滤波算法的正确性和有效性. 相似文献