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結(jié)合PLS表示與隨機(jī)梯度的目標(biāo)優(yōu)化跟蹤

金廣智,  石林鎖,  劉浩,  牟偉杰,  蔡艷平

金廣智, 石林鎖, 劉浩, 牟偉杰, 蔡艷平. 結(jié)合PLS表示與隨機(jī)梯度的目標(biāo)優(yōu)化跟蹤[J]. 電子與信息學(xué)報(bào), 2016, 38(8): 2027-2032. doi: 10.11999/JEIT151082
引用本文: 金廣智, 石林鎖, 劉浩, 牟偉杰, 蔡艷平. 結(jié)合PLS表示與隨機(jī)梯度的目標(biāo)優(yōu)化跟蹤[J]. 電子與信息學(xué)報(bào), 2016, 38(8): 2027-2032. doi: 10.11999/JEIT151082
JIN Guangzhi, SHI Linsuo, LIU Hao, MU Weijie, CAI Yanping. Object Optimization Tracking via PLS Representation and Stochastic Gradient[J]. Journal of Electronics & Information Technology, 2016, 38(8): 2027-2032. doi: 10.11999/JEIT151082
Citation: JIN Guangzhi, SHI Linsuo, LIU Hao, MU Weijie, CAI Yanping. Object Optimization Tracking via PLS Representation and Stochastic Gradient[J]. Journal of Electronics & Information Technology, 2016, 38(8): 2027-2032. doi: 10.11999/JEIT151082

結(jié)合PLS表示與隨機(jī)梯度的目標(biāo)優(yōu)化跟蹤

doi: 10.11999/JEIT151082
基金項(xiàng)目: 

國家自然科學(xué)基金(61501470)

Object Optimization Tracking via PLS Representation and Stochastic Gradient

Funds: 

The National Natural Science Foundation of China (61501470)

  • 摘要: 針對實(shí)際視覺跟蹤中目標(biāo)表觀與前背景的非線性變化，論文提出一種基于偏最小二乘分析(PLS)表示與隨機(jī)梯度的目標(biāo)優(yōu)化跟蹤方法。該方法將目標(biāo)跟蹤轉(zhuǎn)化為表示誤差與分類損失的聯(lián)合優(yōu)化問題。首先，為了提高算法對前背景表觀變化的穩(wěn)定性，利用PLS理論的非線性對目標(biāo)區(qū)域的前背景信息進(jìn)行表達(dá)，并通過空間聚類構(gòu)造多個(gè)線性外觀模型來描述目標(biāo)區(qū)域的動(dòng)態(tài)變化，建立帶約束條件的表觀特征庫；然后，提出一種確定性搜索機(jī)制，構(gòu)造聯(lián)合優(yōu)化目標(biāo)函數(shù)，使表示誤差與分類損失最小化；結(jié)合表觀建模特點(diǎn)，構(gòu)建隨機(jī)梯度分類器，對模型進(jìn)行增量特征更新，最終實(shí)現(xiàn)對目標(biāo)的穩(wěn)定準(zhǔn)確跟蹤。經(jīng)多場景對比實(shí)驗(yàn)驗(yàn)證，該算法能有效應(yīng)對目標(biāo)前背景的多種復(fù)雜變化。
    Abstract: In order to improve the stability and accuracy of the object tracking under nonlinear conditions, an object tracking algorithm based on Partial Least Squares (PLS) representation and stochastic gradient object optimization tracking is proposed. In this method, object tracking is defined as an optimization task that minimizes the representation error and classification loss. Firstly, it expresses object appearance and background information by PLS theory, learns multiple low dimensional and discriminative subspaces to describe the nonlinear appearance changes of the object. Then, a joint optimization objective function based on deterministic search mechanism is proposed. Furthermore, an stochastic gradient classifier based on incremental features updating is proposed, and make sure that it can achieve a stable tracking. Experiments show favorable performance of the proposed algorithm on sequences where the targets undergo a variety complex changes on foreground and background.
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出版歷程
  • 收稿日期:  2015-09-23
  • 修回日期:  2016-05-10
  • 刊出日期:  2016-08-19

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