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为了解决目前跟踪算法在运动目标被遮挡和尺度变换时跟踪效果不佳的问题,提出了一种结合粒子滤波的判别尺度空间跟踪算法。提取相邻两帧的目标区域,计算目标区域的结构相似性并与更新阈值进行比较,从而判断目标是否发生遮挡;其次,若发生遮挡,启用基于颜色分布的粒子滤波算法跟踪目标,反之,用判别尺度空间跟踪算法(DSST)中的位置滤波器确定目标位置;最后,利用尺度滤波器确定目标尺度并根据目标尺度更新粒子滤波的目标模型。经过在OTB2015测试集上进行实验,与判别尺度空间跟踪算法(DSST)、核相关滤波算法(KCF)等主流算法相比该算法的精确度和成功率均有所提高,尤其在发生遮挡后的跟踪效果表现最优。 相似文献
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基于协方差描述子的红外目标粒子滤波跟踪算法 总被引:1,自引:0,他引:1
针对传统协方差矩阵跟踪方法中不能捕获目标旋转变化的问题,提出了一种基于椭圆协方差矩阵的红外目标辅助粒子滤波跟踪方法.首先对矩形协方差矩阵进行扩展,构建了椭圆协方差矩阵描述子,能有效适应目标的尺度和旋转变化,有效提高了目标模型的分辨能力.进而采用改进的李群结构来进行距离度量.在贝叶斯跟踪框架下,采用辅助粒子滤波采样粒子,解决了粒子滤波采样时由于没有利用观测值而造成粒子不能完全覆盖在目标位置附近的问题,最终实现了红外目标的准确定位.实验结果表明该算法简单有效,能准确跟踪尺度和旋转变化的红外目标. 相似文献
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《液晶与显示》2017,(2)
针对目标跟踪中的目标尺度变化、遮挡、光照变化、相似目标混淆等问题,本文提出多特征融合的协同相关跟踪算法。首先,本文用多种特征构建目标外观模型,提高目标模型的鲁棒性,增强跟踪的抗形变能力和抗光照变化能力。然后,利用定点优化策略,解决多模板滤波优化问题,获得最佳滤波参数,通过多模板相关滤波算法估计目标位置,利用改进的尺度池方法解决目标尺度变化问题。最后,利用目标置信度判别跟踪目标是否发生遮挡,当目标发生遮挡时,利用CUR滤波模块重新检测目标,解决遮挡情况下跟踪任务。本文利用OTB-2013数据集中的方法测试本文算法,实验表明本文算法的整体成功率和精确度为0.622和0.830,本文算法在目标发生尺度变化、遮挡、光照变化、相似目标混淆等问题情况下,能准确、可靠地跟踪目标,具有一定研究价值。 相似文献
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针对核相关滤波(KCF)目标跟踪算法在目标发生尺度变化和受到遮挡时无法保证对目标长时间跟踪的问题,提出了一种尺度自适应抗遮挡的长时间目标跟踪算法.首先,将方向梯度直方图(HOG)特征和颜色(CN)特征进行融合并增加一个尺度滤波器用于估计目标的尺度;然后,引入平均峰值相关能量指标(APCE)进行遮挡判断,采用SVM分类器重新检测目标被遮挡后的位置;最后,根据平均峰值相关能量和位置滤波器最大相关响应值选择模型更新策略.选取OTB100和UAV123两个数据集进行实验,结果表明,改进算法能有效地解决目标尺度变化和遮挡等问题,实现对目标的长时间稳定跟踪. 相似文献
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针对经典压缩跟踪算法在目标被遮挡时容易导致目标丢失的问题,提出了一种基于目标遮挡情况下的压缩跟踪算法.该方法首先依据分类器的最大响应值判断目标是否被遮挡.若发生遮挡则利用基于颜色直方图特征的粒子滤波算法进行跟踪预测,即将遮挡前提取的目标颜色直方图与粒子的颜色直方图进行相似性比较.为确保目标再现时能及时准确地捕捉其位置,再利用Harris角点特征进一步验证,并将预测的位置作为目标位置继续压缩跟踪.仿真结果表明,该算法能够准确地判断遮挡的发生,平均跟踪成功率较经典的压缩跟踪算法提高了24%,有效提高了跟踪的鲁棒性. 相似文献
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In this paper, we propose an NCC-based object tracking deep framework, which can be well initialized with the limited target samples in the first frame. The proposed framework contains a pretrained model, online feature fine-tuning layers and tracking processes. The pretrained model provides rich feature representations while online feature fine-tuning layers select discriminative and generic features for the tracked object. We choose normalized cross-correlation as a template tracking layer to perform the tracking process. To enable the learned features representation closely coordinated to the tracked target, we jointly train the feature representation network and tracking processes. In online tracking, an adaptive template and a fixed template are fused to find the optimal tracking results. Scale estimation and a high-confidence model update scheme are perfectly integrated into the framework to adapt to the target appearance changes. The extensive experiments demonstrate that the proposed tracker achieves superior performance compared with other state-of-the-art trackers. 相似文献
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基于均值漂移和粒子滤波的红外目标跟踪 总被引:5,自引:1,他引:4
为了提高红外目标跟踪的准确性和稳健性,提出了基于均值漂移(mean shift)和粒子滤波(PF)相结合的红外目标跟踪方法.在PF理论框架下,使用均值漂移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,在均值漂移迭代过程中对样本权值进行更新.红外目标的状态后验概率分布用重新分配的加权随机样本集表示,对随机样本集使用PF算法实现红外目标运动的跟踪.实验结果表明,和一般PF和均值漂移相比,本文方法具有优越性和更强的稳健性. 相似文献
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Adaptive repetitive control to track variable periodic signals with fixed sampling rate 总被引:2,自引:0,他引:2
Recent research has shown that the repetitive control is very efficient in tracking periodic signals, where it is required that an integer number of samples in each period. However, in some industrial applications where the signal period varies but other requirements force a fixed sample rate, the number of samples per period may be a non-integer. To address this problem, this paper presents a new adaptive repetitive control, which deals with the non-integer samples per period due to the fixed sampling rate. The proposed adaptive repetitive control consists of two portions, the repetitive controller and nominal controller, where the former uses a fictitious sampler operating at a variable sample rate maintained at multiple times of the signal frequency, while the latter uses a fixed sampling rate. Interpolations are utilized to generate the fictitious samples required for the repetitive learning. The nearly perfect tracking was achieved for non-integer samples per period, when a simple linear interpolation is used. The error due to the interpolation is quantified, which is negligible to the residual tracking error. The comparison of the proposed and the existing schemes shows the significant improvement on the tracking performance. The experimental results on the control of a servomotor demonstrate the effectiveness of the proposed schemes. 相似文献
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粒子滤波(Partic le F ilter)是一种处理非线性和非高斯动态系统状态估计的有效技术.提出了一种基于粒子滤波的红外目标稳健跟踪新方法.在粒子滤波理论框架下,红外目标的状态后验概率分布用加权随机样本集表示,通过这些随机样本的Bayesian迭代进化实现红外目标的跟踪.系统状态转移模型选择为简单的二阶自回归模型,并自适应地确定系统噪声方差.红外目标的描述利用目标区域的灰度分布,该灰度分布通过核概率密度估计建立.通过计算参考目标的灰度分布和目标样本的灰度分布之间的Bhattacharyya距离,建立系统观测概率模型.实验结果表明该方法是有效的和稳健的. 相似文献
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Target tracking is an important branch of computer vision, which includes three stages: image sequence optimizing, target expressing and target detecting. The target detecting stage is an important factor that influences the tracking performance. Therefore, how to obtain a more accurate and robust target detecting method becomes an urgent problem. Online sequential extreme learning machine (OSELM) is a kind of online learning method based on extreme learning machine. OSELM completes incremental learning by combining with the existed model when dynamic training samples are arriving. That OSELM has advantages including fast-speed and incremental learning suggests that is suitable for target detecting. Nevertheless, the target detecting process is different from the traditional classification for two causes: (1) target detecting is the dynamic process in that the position and rotation of the target are changing with time, and therefore the original OSELM method fails to obtain the most optimal target object from classified samples, (2) the tracking result frame depends on the previous frame, thus if the noisy sample is used as the target object, it would generates an impact to the tracking performance. To alleviate above-mentioned problems, this paper proposes an interesting and efficient target tracking method based on OSELM. In this method, we obtain the appropriate target object by judging the position relationship between each classified sample and the classification boundary. Moreover, we develop a kind of method that is similar to clustering to avoid tracking drift from noisy samples. The new target tracking method improves the performance remarkably, and eliminates the tracking drift from noisy samples. The proposed method is validated on six kinds of challenging image sequences. 相似文献
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线列阵目标定位同时跟踪的数学模型 总被引:1,自引:1,他引:0
研究了三点等间隔线列阵的目标定位同时跟踪的数学模型,提出了非线性滤波模型;给出了m点线列阵目标定位同时跟踪的数学模型;提出了2m-1点线列阵目标定位同时跟踪的数学模型,模型更为简化;最后,分析了"先定位后跟踪"与"定位同时跟踪"处理途径,提出由纯时延与非线性滤波相结合,为线列阵TMA研究提供了新的途径。 相似文献
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为解决变尺度目标的跟踪问题,本文基于压缩感知理论设计了一种具有目标尺度不变性的目标跟踪方法。该方法首先通过插值的方式将初始帧中要跟踪的目标扩展细化至设定的模板图像大小,提取其压缩感知变换后的低维Haar-like特征作为模板特征并初始化分类器,其次利用卡尔曼滤波对待跟踪的图像帧中目标所在位置和尺度变化趋势进行预测,然后在预测目标所在位置周围提取多个不同尺度的待测目标样本并提取其压缩感知变换后的低维Haar-like特征,最后将这些特征送入分类器进行分类得到真实目标并更新分类器。经过实验验证,本文所设计的跟踪方法的平均跟踪成功率为77%,平均中心位置误差为12像素。能够实现对运动过程中发生尺度变化的目标的有效跟踪。 相似文献
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基于自训练的判别式目标跟踪算法使用分类器的预测结果更新分类器自身,容易累积分类错误,从而导致漂移问题。为了克服自训练跟踪算法的不足,该文提出一种基于在线半监督boosting的协同训练目标跟踪算法(简称Co-SemiBoost),其采用一种新的在线协同训练框架,利用未标记样本协同训练两个特征视图中的分类器,同时结合先验模型和在线分类器迭代预测未标记样本的类标记和权重。该算法能够有效提高分类器的判别能力,鲁棒地处理遮挡、光照变化等问题,从而较好地适应目标外观的变化。在若干个视频序列的实验结果表明,该算法具有良好的跟踪性能。 相似文献
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Recent years have witnessed several modified discriminative correlation filter (DCF) models exhibiting excellent performance in visual tracking. A fundamental drawback to these methods is that rotation of the target is not well addressed which leads to model deterioration. In this paper, we propose a novel rotation-aware correlation filter to address the issue. Specifically, samples used for training of the modified DCF model are rectified when rotation occurs, rotation angle is effectively calculated using phase correlation after transforming the search patch from Cartesian coordinates to the Log-polar coordinates, and an adaptive selection mechanism is further adopted to choose between a rectified target patch and a rectangular patch. Moreover, we extend the proposed approach for robust tracking by introducing a simple yet effective Kalman filter prediction strategy. Extensive experiments on five standard benchmarks show that the proposed method achieves superior performance against state-of-the-art methods while running in real-time on single CPU. 相似文献