计算机科学 ›› 2018, Vol. 45 ›› Issue (3): 69-75.doi: 10.11896/j.issn.1002-137X.2018.03.012

• 第十届全国几何设计与计算学术会议 • 上一篇    下一篇

协同结构稀疏重构的判别性视觉跟踪

游思思,应龙,郭文,丁昕苗,华臻   

  1. 山东工商学院信息与电子工程学院 山东 烟台264009,南京林业大学信息科学与技术学院 南京210037,山东工商学院信息与电子工程学院 山东 烟台264009,山东工商学院信息与电子工程学院 山东 烟台264009,山东工商学院信息与电子工程学院 山东 烟台264009
  • 出版日期:2018-03-15 发布日期:2018-11-13
  • 基金资助:
    本文受国家自然科学基金(61572296,7,61303086,5),山东省自然科学基金(ZR2015FL020),模式识别国家重点实验室开放课题(201600024)资助

Discriminative Visual Tracking by Collaborative Structural Sparse Reconstruction

YOU Si-si, YING Long, GUO Wen, DING Xin-miao and HUA Zhen   

  • Online:2018-03-15 Published:2018-11-13

摘要: 基于稀疏表示的表观似然模型在目标跟踪领域具有广泛的应用,但是这种单一产生式目标表观模型并未考虑完整的判别性结构信息,容易受复杂背景的干扰。为了缓解由该问题造成的目标跟踪漂移,提出了一种目标表观字典和背景字典协同结构稀疏重构优化的视觉跟踪方法。通过构建一个有判别力的基于稀疏表示的表观似然模型,实现了对目标表观模型更为准确的描述。通过合理选择约束候选目标区域和候选背景区域的稀疏系数,在表观似然模型中引入判别式信息,以进一步揭示候选目标区域的潜在相关性和候选背景区域的结构关系,从而更加准确地学习候选目标区域的表观模型。大量有挑战性的视频序列上的实验结果验证了算法在复杂背景下跟踪的鲁棒性,与其他相关算法的对比实验也体现了该算法的优越性。

关键词: 稀疏协同重构,稀疏表达,图方法,判别性跟踪

Abstract: Though the appearance likelihood model based on sparse representation has been widely applied in visual tracking,the single generation object representation model can easily be interrupted by background clutter due to not considering the full discriminative structural information.In order to alleviate the drift problem of the visual tracking,this paper presented a novel tracking method based on collaborative sparse reconstruction of object appearance dictionary and background dictionary.This paper achieved a more accurate description of the target appearance model by constructing a discriminative appearance likelihood model based on sparse representation.Then,it embedded discriminative information into the appearance likelihood model by a reasonable method of selecting the sparse coefficients of candidate target region and candidate background region.By that way,it can explore the potential correlation of candidate target region and the structure relation of candidate background region,so as to learn the appearance model of candidate target area more accurately.Many experimental results in challenging sequence verify the robustness of this method.The proposed tracker outperforms excellent performance in comparison with other state-of-the-art trackers.

Key words: Collaborative sparse reconstruction,Sparse representation,Graphic methods,Discriminative tracking

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