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We present an algorithm for acquiring the 3D surface geometry and motion of a dynamic piecewise‐rigid object using a single depth video camera. The algorithm identifies and tracks the rigid components in each frame, while accumulating the geometric information acquired over time, possibly from different viewpoints. The algorithm also reconstructs the dynamic skeleton of the object, thus can be used for markerless motion capture. The acquired model can then be animated to novel poses. We show the results of the algorithm applied to synthetic and real depth video. 相似文献
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《Real》1997,3(6):415-432
Real-time motion capture plays a very important role in various applications, such as 3D interface for virtual reality systems, digital puppetry, and real-time character animation. In this paper we challenge the problem of estimating and recognizing the motion of articulated objects using theoptical motion capturetechnique. In addition, we present an effective method to control the articulated human figure in realtime.The heart of this problem is the estimation of 3D motion and posture of an articulated, volumetric object using feature points from a sequence of multiple perspective views. Under some moderate assumptions such as smooth motion and known initial posture, we develop a model-based technique for the recovery of the 3D location and motion of a rigid object using a variation of Kalman filter. The posture of the 3D volumatric model is updated by the 2D image flow of the feature points for all views. Two novel concepts – the hierarchical Kalman filter (KHF) and the adaptive hierarchical structure (AHS) incorporating the kinematic properties of the articulated object – are proposed to extend our formulation for the rigid object to the articulated one. Our formulation also allows us to avoid two classic problems in 3D tracking: the multi-view correspondence problem, and the occlusion problem. By adding more cameras and placing them appropriately, our approach can deal with the motion of the object in a very wide area. Furthermore, multiple objects can be handled by managing multiple AHSs and processing multiple HKFs.We show the validity of our approach using the synthetic data acquired simultaneously from the multiple virtual camera in a virtual environment (VE) and real data derived from a moving light display with walking motion. The results confirm that the model-based algorithm works well on the tracking of multiple rigid objects. 相似文献
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基于DSP的运动目标跟踪系统 总被引:6,自引:0,他引:6
描述了一种以TMS320C6701数字信号处理器为核心的高速图像处理板和图像实时采集卡及摄像头构成的实时运动跟踪系统。在对采集的实时图像序列进行如十预处理后.采用了金字塔结构的图像存储方式和特征点跟踪算法埘运动目标进行跟踪.通过对特征点的运算得到目标运动的偏差怍为摄像头运动的参数,是后根据这些参数控制摄像云台持续跟踪运动目标的移动,最后还给出了在复杂背景下跟踪人体的实验结果。 相似文献
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针对单视角像机的视野局限性以及连续跟踪特定目标时运动目标实时检测的困难,提出了一种离散化活动像机运动的方法。先离散化像机运动;建立像机预置位表和背景索引表;然后,利用目标位置信息和运动信息判断像机转动情况,并结合对应的控制原理,实现了像机的调度;最后,利用通过计算机视觉方法确定了特定目标在不同离散空间的对应关系,实现了对特定目标大范围的主动跟踪和运动目标的实时检测。实验结果表明,对于复杂场景中特定运动目标大范围的主动跟踪具有较好的鲁棒性和实时性,并且可以实时提取场景中的运动区域。 相似文献
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视频图像序列中运动目标的提取与跟踪 总被引:1,自引:0,他引:1
提出一种摄像机静止条件下视频图像运动目标提取与跟踪的方法。本文首先提出了一种有效的阴影去除算法检测运动目标,然后采用基于目标颜色直方图的相关匹配,使用Bhattacharyya系数度量目标模型与预测模型间的相似度,选出最优相似模型作为当前的目标模型,实现了对目标的跟踪。实验结果表明该算法是可行有效的。 相似文献
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曹丽武 《计算机与数字工程》2011,39(3):125-127,187
针对人体目标运动的非刚体性而难以跟踪问题,提出了一种基于视频图像的人体跟踪方法。首先获取人体目标,选取目标若干子区域作为跟踪模板,用Kalman滤波和最小差值法作为相似性测度的相关跟踪对各子区域进行精确定位,最后将各个子区域跟踪结果进行位置融合得到最终的人体目标位置。实验表明,该方法对人体目标细节性形变和大的形变均能进行有效跟踪。 相似文献
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对现有基于压缩感知的视频目标跟踪系统进行改进,提出一种可实现隐私保护的监控视频目标跟踪系统。在编码端采用结构化随机矩阵,以提高随机采样矩阵的生成速度。在解码端采用GPSR-BB算法,以提高系统抗噪性。利用粒子滤波器算法实现目标跟踪,减少跟踪结果误差对压缩感知恢复算法准确性的影响和分析时间。实验结果表明,该系统在实现隐私保护的同时,提高了系统对光照的鲁棒性,在室内外光照条件下均能准确跟踪目标。与BP和Lasso方法相比,分别可节约30.3%和51.6%的处理时间。 相似文献
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为了更加准确地预测对象的位置和运动,利用刚体运动模型导出最佳Kalman系数,通过Kalman反馈滤波器对Moscheni等人提出的视频对象分割与跟踪算法进行改进,提出了一种将离散Kalman滤波技术用于视频序列的对象跟踪方法。这种方法可用于有关场景描述的各种应用领域中,如在机器视觉的研究中,对动态场景进行分析与理解;在基于对象的视频编码中(如MPEG-4),对视频对象进行分割后,分别进行编码,从而改善编码的可分级性及编码效率。实验结果表明,采用这种方法可以有效地改善时间-空间分割和目标跟踪,有助于更好地理解动态场景,并表现出良好的鲁棒性。 相似文献
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根据增强现实在视频对象跟踪中的应用需求,提出一种综合利用尺度不变特征变换(SIFT)算子、K聚类算法和轮廓检测的视频对象跟踪算法。该算法利用简易SIFT获得输入图像的特征点,通过K聚类算法获得可能的对象聚类,并采用改进的轮廓处理方法得到对象边界,移除孤立点,确定对象特征点,在对象特征点中获取增强现实应用中需要的注册点。在关键帧匹配中,只要使用对象特征点进行对象匹配。实验结果表明,该算法具有运行速度快、匹配正确率高的特点,能满足增强现实视频应用的注册需求。 相似文献
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《Real》2002,8(2):145-155
Real-time object tracking is recently becoming more and more important in the field of video analysis and processing. Applications like traffic control, user–computer interaction, on-line video processing and production and video surveillance need reliable and economically affordable video tracking tools. It seems, however, that most of the available solutions are computationally intensive and sometimes require expensive video hardware, quite often without guaranteeing a suitable level of reliability. In this paper, we present a new approach to real-time object tracking from colour video sequences. It relies on contours in order to track the shape, position and orientation of objects, without exploiting snakes or “traditional” active contours. A closed-loop control approach is adopted to enforce motion tracking stability, while a separate shape model is maintained, featuring a two-stage model and a median filtering technique to cope with temporary occlusions and noise. The system was tested in several different environments with different constraints, and gave very encouraging performance. Experimental results are reported and commented on. 相似文献
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一种有效的体育视频目标跟踪算法 总被引:1,自引:0,他引:1
论文研究了面向体育视频的运动目标跟踪技术,提出了一种最优化的混合跟踪方法。首先,采用粒子滤波算法来预测运动目标的初略位置,通过比较预测位置上的目标同目标模型之间的相似度,当相似度小于一定的阀值时,认为目标运动模型发生了根本变化,需要启用新的运动模型;当相似度大于一定的阀值时则认为目标运动模型没有发生大的变化,不需要启用新的运动模型,通过这种方式找到目标的最优化运动模型。最后将最优化的运动模型用于基于核的均值转移算法中,从而获得运动目标的精确位置。 相似文献
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实时图像的运动目标检测及跟踪 总被引:2,自引:0,他引:2
在复杂背景的运动目标实时检测算法的研究中,由于目标受到外界环境影响,目标不能正确提取。针对克服背景干扰因素提取,干净的目标像素,大多数背景建模与背景更新算法计算复杂,难以满足视频监控的实时要求。为解决上述问题,提出一种根据像素特征的背景差法,将目标的边缘特征融入减背景算法,通过对离散的目标边缘梯度像素进行网格密度聚类法实现目标像素的提取,采用改进的均值漂移跟踪算法,在DM642平台上实现目标检测与跟踪。实验结果表明,改进的算法可以有效的克服光线变化、背景抖动、噪声等问题,实时检测、跟踪多个目标,并能解决目标遮挡问题。 相似文献
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《Real》1998,4(1):21-40
This paper describes how image sequences taken by a moving video camera may be processed to detect and track moving objects against a moving background in real-time. The motion segmentation and shape tracking system is known as a scene segmenter establishing tracking, version 2 (ASSET-2). Motion is found by tracking image features, and segmentation is based on first-order (i.e. six-parameter) flow fields. Shape tracking is performed using two-dimensional radial map representations. The system runs in real-time, and is accurate and reliable. It requires no camera calibration and no knowledge of the camera's motion. 相似文献