结合GLCM与三阶张量建模的在线目标跟踪

金广智; 石林锁; 崔智高; 刘浩; 牟伟杰

doi:10.11999/JEIT151108

结合GLCM与三阶张量建模的在线目标跟踪

doi: 10.11999/JEIT151108

基金项目:

国家自然科学基金(61501470)

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- 文章访问数: 1708
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- 被引次数: 0
出版历程
- 收稿日期: 2015-09-29
- 修回日期: 2016-03-03
- 刊出日期: 2016-07-19

Online Object Tracking Based on Gray-level Co-occurrence Matrix and Third-order Tensor

Funds:

The National Natural Science Foundation of China (61501470)

摘要

摘要: 为提高目标跟踪算法对多种目标表观变化场景的自适应能力和跟踪精度，论文提出一种结合灰度共生(GLCM)与三阶张量建模的目标优化跟踪算法。该算法首先提取目标区域的灰度信息，通过GLCM的高区分度特征对目标进行二元超分描述，并结合三阶张量理论融合目标区域的多视图信息，建立起目标的三阶张量表观模型。然后利用线性空间理论对表观模型进行双线性展开，通过在线模型特征值描述与双线性空间的增量特征更新，明显降低模型更新时的运算量。跟踪环节，建立二级联合跟踪机制，结合当前时刻信息通过在线权重估计构建动态观测模型，以真实目标视图为基准建立静态观测模型对跟踪估计动态调整，以避免误差累积出现跟踪漂移，最终实现对目标的稳定跟踪。通过与典型算法进行多场景试验对比，表明该算法能够有效应对多种复杂场景下的运动目标跟踪，平均跟踪误差均小于9像素。
- 目标跟踪 /
- 灰度共生 /
- 三阶张量 /
- 线性空间 /
- 在线跟踪
Abstract: In order to improve the stability and accuracy of the object tracking under different conditions, an online object tracking algorithm based on Gray-Level Co-occurrence Matrix (GLCM) and third-order tensor is proposed. First, the algorithm extracts the gray-level information of target area to describe the two high discrimination features of target by GLCM, the dynamic information about target changing is fused by third-order tensor theory, and the third-order tensor appearance model of the object is constructed. Then, it uses bilinear space theory to expand the appearance model, and implements the incremental learning. Updating of model by online models characteristic value description, thus computation of the model updating is greatly reduced. Meanwhile, the static observation model and adaptive observation model are constructed, and secondary combined stable tracking of object is achieved by dynamic matching of two observation models. Experimental results indicate that the proposed algorithm can effectively deal with the moving object tracking on a variety of challenging scenes, and the average tracking error is less than 9 pixels.
- Object tracking /
- Gray-Level Co-occurrence Matrix (GLCM) /
- Third-order tensor /
- Linear space /
- Online tracking

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