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結(jié)合GLCM與三階張量建模的在線目標(biāo)跟蹤

金廣智 石林鎖 崔智高 劉浩 牟偉杰

金廣智, 石林鎖, 崔智高, 劉浩, 牟偉杰. 結(jié)合GLCM與三階張量建模的在線目標(biāo)跟蹤[J]. 電子與信息學(xué)報(bào), 2016, 38(7): 1609-1615. doi: 10.11999/JEIT151108
引用本文: 金廣智, 石林鎖, 崔智高, 劉浩, 牟偉杰. 結(jié)合GLCM與三階張量建模的在線目標(biāo)跟蹤[J]. 電子與信息學(xué)報(bào), 2016, 38(7): 1609-1615. doi: 10.11999/JEIT151108
JIN Guangzhi, SHI Linsuo, CUI Zhigao, LIU Hao, MU Weijie. Online Object Tracking Based on Gray-level Co-occurrence Matrix and Third-order Tensor[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1609-1615. doi: 10.11999/JEIT151108
Citation: JIN Guangzhi, SHI Linsuo, CUI Zhigao, LIU Hao, MU Weijie. Online Object Tracking Based on Gray-level Co-occurrence Matrix and Third-order Tensor[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1609-1615. doi: 10.11999/JEIT151108

結(jié)合GLCM與三階張量建模的在線目標(biāo)跟蹤

doi: 10.11999/JEIT151108
基金項(xiàng)目: 

國家自然科學(xué)基金(61501470)

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: 為提高目標(biāo)跟蹤算法對(duì)多種目標(biāo)表觀變化場(chǎng)景的自適應(yīng)能力和跟蹤精度,論文提出一種結(jié)合灰度共生(GLCM)與三階張量建模的目標(biāo)優(yōu)化跟蹤算法。該算法首先提取目標(biāo)區(qū)域的灰度信息,通過GLCM的高區(qū)分度特征對(duì)目標(biāo)進(jìn)行二元超分描述,并結(jié)合三階張量理論融合目標(biāo)區(qū)域的多視圖信息,建立起目標(biāo)的三階張量表觀模型。然后利用線性空間理論對(duì)表觀模型進(jìn)行雙線性展開,通過在線模型特征值描述與雙線性空間的增量特征更新,明顯降低模型更新時(shí)的運(yùn)算量。跟蹤環(huán)節(jié),建立二級(jí)聯(lián)合跟蹤機(jī)制,結(jié)合當(dāng)前時(shí)刻信息通過在線權(quán)重估計(jì)構(gòu)建動(dòng)態(tài)觀測(cè)模型,以真實(shí)目標(biāo)視圖為基準(zhǔn)建立靜態(tài)觀測(cè)模型對(duì)跟蹤估計(jì)動(dòng)態(tài)調(diào)整,以避免誤差累積出現(xiàn)跟蹤漂移,最終實(shí)現(xiàn)對(duì)目標(biāo)的穩(wěn)定跟蹤。通過與典型算法進(jìn)行多場(chǎng)景試驗(yàn)對(duì)比,表明該算法能夠有效應(yīng)對(duì)多種復(fù)雜場(chǎng)景下的運(yùn)動(dòng)目標(biāo)跟蹤,平均跟蹤誤差均小于9像素。
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出版歷程
  • 收稿日期:  2015-09-29
  • 修回日期:  2016-03-03
  • 刊出日期:  2016-07-19

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