共查询到17条相似文献,搜索用时 140 毫秒
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基于局部特征组合的目标跟踪算法 总被引:1,自引:0,他引:1
为了克服目前大多数观测模型在小样本空间中鲁棒性不高的弱点,文中在粒子滤波框架下提出基于局部特征组合的粒子滤波视频跟踪算法。局部特征能更有效描述目标模板细节信息,可降低特征匹配中目标形变、光照变化和部分遮挡的影响。该方法借鉴混合高斯模型思想,采用多模式描述有效局部观测信息,这种融合策略更加准确可靠,能够较好地通过最新观测减轻了粒子退化现象,从而提高目标跟踪效率。小样本空间一定程度上降低了粒子数量和计算代价。实验结果表明该算法相比单一特征或一般多特征融合跟踪算法具有优越性,并能实现复杂场景下的目标跟踪。 相似文献
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提出了一种将粒子滤波和CamShift相结合的多特征视觉跟踪方法.通过CamShift对粒子的位置和尺度同时进行优化,使得跟踪窗口能随着目标尺度的大小变化相应调整.同时采用自适应方式将颜色信息和运动信息在CamShift优化的粒子滤波框架下有效结合起来.该方法使用CamShift对粒子传播进行优化,每个粒子都收敛到目标附近,粒子的有效性得到提高.实验结果表明,使用10个粒子的CamShiit优化的粒子滤波的跟踪误差小于100个粒子的传统粒子滤波的跟踪误差.并且由于多特征的使用,目标在受到背景相似物体干扰和场景光线发生显著变化等情况下仍能实现稳定的跟踪.用较少的粒子就能实现稳定的跟踪,减少了计算代价,提高了跟踪的鲁棒性. 相似文献
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一种基于粒子滤波的特征融合跟踪算法 总被引:4,自引:1,他引:3
针对单纯的基于颜色的跟踪方法在复杂背景下会导致跟踪失败的问题,本文提出一种基于粒子滤波的特征融合跟踪算法。颜色直方图是对目标的全局描述,而方向梯度直方图包含了一定的结构信息,二者可以互为补充,因此本文算法同时用颜色直方图和方向梯度直方图来描述目标,在粒子滤波框架下将目标颜色和梯度信息有机结合,并自适应更新。实验表明,本文算法不仅提高了跟踪精度,而且具有较强的鲁棒性。 相似文献
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实际人脸跟踪过程中,光照和姿态的变化、背景颜色干扰等因素都会极大地削弱颜色特征的有效性,从而造成跟踪的不稳定.针对该问题,本文提出了一种以颜色和轮廓分布为线索的粒子滤波人脸跟踪算法.该算法主要有三个方面的特点:第一,在粒子滤波基本框架下,引入新的用直方图描述人脸轮廓的方法,有效解决了光照、人脸旋转、部分遮挡问题对跟踪的影响,并且能及时有效地重新捕获由于大面积遮挡等原因而丢失的目标.同时采用实时调整每帧图像特征点个数,有效提高了跟踪效率.第二,针对背景干扰问题,提出了一种抑制相似背景颜色干扰的方法.第三,本文还提出实时更新模板的方法来提高跟踪的准确性.实验证明本文算法对人脸跟踪具有很好的效果. 相似文献
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Shao J Porikli F Chellappa R 《Journal of the Optical Society of America. A, Optics, image science, and vision》2007,24(8):2109-2121
We present an algorithm for nonrigid contour tracking in heavily cluttered background scenes. Based on the properties of nonrigid contour movements, a sequential framework for estimating contour motion and deformation is proposed. We solve the nonrigid contour tracking problem by decomposing it into three subproblems: motion estimation, deformation estimation, and shape regulation. First, we employ a particle filter to estimate the global motion parameters of the affine transform between successive frames. Then we generate a probabilistic deformation map to deform the contour. To improve robustness, multiple cues are used for deformation probability estimation. Finally, we use a shape prior model to constrain the deformed contour. This enables us to retrieve the occluded parts of the contours and accurately track them while allowing shape changes specific to the given object types. Our experiments show that the proposed algorithm significantly improves the tracker performance. 相似文献
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Automatic target tracking in FLIR image sequences using intensity variation function and template modeling 总被引:3,自引:0,他引:3
A novel automatic target tracking (ATT) algorithm for tracking targets in forward-looking infrared (FLIR) image sequences is proposed in this paper. The proposed algorithm efficiently utilizes the target intensity feature, surrounding background, and shape information for tracking purposes. This algorithm involves the selection of a suitable subframe and a target window based on the intensity and shape of the known reference target. The subframe size is determined from the region of interest and is constrained by target size, target motion, and camera movement. Then, an intensity variation function (IVF) is developed to model the target intensity profile. The IVF model generates the maximum peak value where the reference target intensity variation is similar to the candidate target intensity variation. In the proposed algorithm, a control module has been incorporated to evaluate IVF results and to detect a false alarm (missed target). Upon detecting a false alarm, the controller triggers another algorithm, called template model (TM), which is based on the shape knowledge of the reference target. By evaluating the outputs from the IVF and TM techniques, the tracker determines the real coordinates of one or more targets. The proposed technique also alleviates the detrimental effects of camera motion, by appropriately adjusting the subframe size. Experimental results using real-life long-wave and medium-wave infrared image sequences are shown to validate the robustness of the proposed technique. 相似文献
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基于自适应前景分割及粒子滤波的人体运动跟踪 总被引:2,自引:0,他引:2
提出了在图像序列中用自适应前景分割及粒子滤波对人体的3-D运动轨迹进行跟踪的方法.首先建立了像素点的高斯模型,并结合图像帧间的差分信息以及灰度分布的先验概率等因素完成了图像中人体的自适应分割.根据所得到的分割结果建立了透视投影下的运动平面跟踪模型.根据投影过程的非线性以及图像中噪声分布的未知性,提出了粒子滤波的跟踪方法,并最终得到了人体运动平面的3-D轨迹.实际人体运动图像序列的实验证明,本文方法能有效地跟踪人体运动的3-D轨迹,并反映出在此跟踪问题上粒子滤波比传统的扩展卡尔曼滤波更具优势. 相似文献
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We address the problem of body pose tracking in a scenario of multiple camera setup with the aim of recovering body motion robustly and accurately. The tracking is performed on three-dimensional (3D) space using 3D data, including colored volume and 3D optical flow, which are reconstructed at each time step. We introduce strategies to compute multiple camera-based 3D optical flow and have attained efficient and robust 3D motion estimation. Body pose estimation starts with a prediction using 3D optical flow and then is changed to a lower-dimensional global optimization problem. Our method utilizes a voxel subject-specific body model, exploits multiple 3D image cues, and incorporates physical constraints into a stochastic particle-based search initialized from the deterministic prediction and stochastic sampling. It leads to a robust 3D pose tracker. Experiments on publicly available sequences show the robustness and accuracy of our approach. 相似文献