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局部感知下的稀疏優(yōu)化目標(biāo)跟蹤方法

劉大千 劉萬軍 費(fèi)博雯

劉大千, 劉萬軍, 費(fèi)博雯. 局部感知下的稀疏優(yōu)化目標(biāo)跟蹤方法[J]. 電子與信息學(xué)報(bào), 2018, 40(2): 272-281. doi: 10.11999/JEIT170473
引用本文: 劉大千, 劉萬軍, 費(fèi)博雯. 局部感知下的稀疏優(yōu)化目標(biāo)跟蹤方法[J]. 電子與信息學(xué)報(bào), 2018, 40(2): 272-281. doi: 10.11999/JEIT170473
LIU Daqian, LIU Wanjun, FEI Bowen. Object Tracking Method Based on Sparse Optimization of Local Sensing[J]. Journal of Electronics & Information Technology, 2018, 40(2): 272-281. doi: 10.11999/JEIT170473
Citation: LIU Daqian, LIU Wanjun, FEI Bowen. Object Tracking Method Based on Sparse Optimization of Local Sensing[J]. Journal of Electronics & Information Technology, 2018, 40(2): 272-281. doi: 10.11999/JEIT170473

局部感知下的稀疏優(yōu)化目標(biāo)跟蹤方法

doi: 10.11999/JEIT170473
基金項(xiàng)目: 

國家自然科學(xué)基金(61172144),遼寧省科技攻關(guān)計(jì)劃項(xiàng)目(2012216026)

Object Tracking Method Based on Sparse Optimization of Local Sensing

Funds: 

The National Natural Science Foundation of China (61172144), The Science and Technology Foundation of Liaoning Province (2012216026)

  • 摘要: 針對傳統(tǒng)稀疏表示跟蹤算法在復(fù)雜背景中易出現(xiàn)跟蹤漂移問題,該文提出一種局部感知下的稀疏優(yōu)化目標(biāo)跟蹤方法。首先,將首幀確定的目標(biāo)區(qū)域進(jìn)行非重疊均勻分割,并利用目標(biāo)的全局特征和局部特征聯(lián)合建模。然后,提出一種局部感知校驗(yàn)方法約束稀疏優(yōu)化匹配過程,從而確定最優(yōu)匹配樣本。最后,在模板更新中提出一種決策方法對遮擋進(jìn)行檢測,并針對不同遮擋情況采取相應(yīng)的更新策略,使得更新后的模板集更加完善。實(shí)驗(yàn)在10個(gè)標(biāo)準(zhǔn)庫視頻序列中測試,并與目前較流行的目標(biāo)跟蹤算法在跟蹤效果、成功率等方面進(jìn)行比較,實(shí)驗(yàn)結(jié)果表明,提出的跟蹤方法在局部遮擋、目標(biāo)形變、復(fù)雜背景等條件下跟蹤準(zhǔn)確、適應(yīng)性強(qiáng)。
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出版歷程
  • 收稿日期:  2017-05-17
  • 修回日期:  2017-08-01
  • 刊出日期:  2018-02-19

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