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浙江大学学报(工学版)
J4  2009, Vol. 43 Issue (09): 1597-1603    DOI: 10.3785/j.issn.1008973X.2009.09.009
    
Threedimensional head pose tracking using registration and multiscale appearance model
 DIAO Gang-Jiang, CHEN Ling, CHEN Gen-Cai
（College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China）
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Abstract  

Scale invariant feature transform (SIFT) local descriptor based registration algorithm and multiscale viewbased appearance model were used aiming at the problem of large head movement tracking. The SIFT local descriptor based registration algorithm can estimate the pose change between two frames even when head scale was also changed by matching salient SIFT features between two intensity images. The multiscale viewbased appearance model was employed to reduce the drift accumulation during tracking in large range. The model selected key frames online when the head underwent different motions and the tracker bounded the drift of current frame by employing multiple registrations approach. Experimental results show that the method is not only accurate (4 °RMS error), but also robust with respect to the movement along the Z axis was about 1 m and the subject returned to the visual field of camera after abrupt leaving．

CLC:  TP 391  
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DIAO Gang-Jiang, CHEN Ling, CHEN Gen-Cai. Threedimensional head pose tracking using registration and multiscale appearance model. J4, 2009, 43(09): 1597-1603.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008973X.2009.09.009     OR     http://www.zjujournals.com/eng/Y2009/V43/I09/1597


基于注册和多尺度表观模型的三维头部跟踪

针对人体大范围运动下的头部姿态跟踪问题,提出一种基于尺度不变特征变换（SIFT）局部描述符注册和多尺度表观模型的三维头部姿态跟踪方法.基于SIFT局部描述符的注册算法通过在两帧灰度图像间进行特征点匹配计算两帧间的头部运动,在两帧人脸图像的尺度有一定变化时仍可得到精确结果.多尺度视角表观模型可以减少大范围跟踪时的误差累积,该模型在线选取具有不同头部姿态的关键帧,并通过多次注册的方法来减少当前帧的误差累积.实验结果表明,该方法不仅跟踪结果准确（均方根（RMS）误差为4 °）,而且在人体前后运动约1 m和头部进出摄像机视角情况下均很鲁棒.

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