结合特征点匹配及深度网络检测的运动跟踪

陈姝, 梁文章

陈姝, 梁文章. 结合特征点匹配及深度网络检测的运动跟踪[J]. 电子科技大学学报, 2016, 45(2): 246-251.
引用本文: 陈姝, 梁文章. 结合特征点匹配及深度网络检测的运动跟踪[J]. 电子科技大学学报, 2016, 45(2): 246-251.
CHEN Shu, LIANG Wen-zhang. Object Tracking by Combining Feature Correspondences Matching with Deep Neural Network Detection[J]. Journal of University of Electronic Science and Technology of China, 2016, 45(2): 246-251.
Citation: CHEN Shu, LIANG Wen-zhang. Object Tracking by Combining Feature Correspondences Matching with Deep Neural Network Detection[J]. Journal of University of Electronic Science and Technology of China, 2016, 45(2): 246-251.

结合特征点匹配及深度网络检测的运动跟踪

详细信息
  • 中图分类号: TP391

Object Tracking by Combining Feature Correspondences Matching with Deep Neural Network Detection

  • 摘要: 通过样本学习得到的目标先验视觉信息可以对目标进行高效表示,在目标跟踪中通过充分利用这些先验知识提高跟踪精度。基于此,提出一种利用离线训练结果进行在线跟踪的算法,首先利用深度神经网络通过样本学习目标的视觉先验,然后跟踪在贝叶斯推理框架下进行,在跟踪过程中将目标视觉先验用作目标的外观表示,跟踪结果由粒子滤波顺序得到。为了防止跟踪漂移,通过特征点匹配建立系统的状态模型,并且将目标分解成子目标进行相似度量,提高算法抗局部遮挡能力。在多个公开测试集上实验表明,该算法可以提高目标跟踪精度,防止跟踪漂移,实现长序列可靠跟踪。
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  • 期刊类型引用(1)

    1. 孟博, 鲁金钿, 王德军, 何旭东. 安全协议实施安全性分析综述. 山东大学学报(理学版). 2018(01): 1-18 . 百度学术

    其他类型引用(2)

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出版历程
  • 刊出日期:  2016-04-14

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