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基于在線判別式字典學(xué)習(xí)的魯棒視覺(jué)跟蹤

薛模根 朱虹 袁廣林

薛模根, 朱虹, 袁廣林. 基于在線判別式字典學(xué)習(xí)的魯棒視覺(jué)跟蹤[J]. 電子與信息學(xué)報(bào), 2015, 37(7): 1654-1659. doi: 10.11999/JEIT141325
引用本文: 薛模根, 朱虹, 袁廣林. 基于在線判別式字典學(xué)習(xí)的魯棒視覺(jué)跟蹤[J]. 電子與信息學(xué)報(bào), 2015, 37(7): 1654-1659. doi: 10.11999/JEIT141325
Xue Mo-gen, Zhu Hong, Yuan Guang-lin. Robust Visual Tracking Based on Online Discrimination Dictionary Learning[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1654-1659. doi: 10.11999/JEIT141325
Citation: Xue Mo-gen, Zhu Hong, Yuan Guang-lin. Robust Visual Tracking Based on Online Discrimination Dictionary Learning[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1654-1659. doi: 10.11999/JEIT141325

基于在線判別式字典學(xué)習(xí)的魯棒視覺(jué)跟蹤

doi: 10.11999/JEIT141325
基金項(xiàng)目: 

國(guó)家自然科學(xué)基金(61175035, 61379105),中國(guó)博士后科學(xué)基金(2014M562535)和安徽省自然科學(xué)基金(1508085QF114)

Robust Visual Tracking Based on Online Discrimination Dictionary Learning

  • 摘要: 現(xiàn)有子空間跟蹤方法較好地解決了目標(biāo)表觀變化和遮擋問(wèn)題,但是它對(duì)復(fù)雜背景下目標(biāo)跟蹤的魯棒性較差。針對(duì)此問(wèn)題,該文首先提出一種基于Fisher準(zhǔn)則的在線判別式字典學(xué)習(xí)模型,利用塊坐標(biāo)下降和替換操作設(shè)計(jì)了該模型的在線學(xué)習(xí)算法用于視覺(jué)跟蹤模板更新。其次,定義候選目標(biāo)編碼系數(shù)與目標(biāo)樣本編碼系數(shù)均值之間的距離為系數(shù)誤差,提出以候選目標(biāo)的重構(gòu)誤差與系數(shù)誤差的組合作為粒子濾波的觀測(cè)似然跟蹤目標(biāo)。實(shí)驗(yàn)結(jié)果表明:與現(xiàn)有跟蹤方法相比,該文跟蹤方法具有較強(qiáng)的魯棒性和較高的跟蹤精度。
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出版歷程
  • 收稿日期:  2014-10-20
  • 修回日期:  2015-02-09
  • 刊出日期:  2015-07-19

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