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
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1.
(陸軍軍官學(xué)院偏振光成像探測(cè)技術(shù)安徽省重點(diǎn)實(shí)驗(yàn)室 合肥 230031) ②(陸軍軍官學(xué)院十一系 合肥 230031)
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
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1.
(Anhui Province Key Laboratory of Polarization Imaging Detection Technology, Army Officer Academy of PLA, Hefei 230031, China)
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2.
(Anhui Province Key Laboratory of Polarization Imaging Detection Technology, Army Officer Academy of PLA, Hefei 230031, China)
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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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關(guān)鍵詞:
- 視覺(jué)跟蹤 /
- 模板更新 /
- 字典學(xué)習(xí) /
- 觀測(cè)似然
Abstract: The existing subspace tracking methods have well solved appearance changes and occlusions. However, they are weakly robust to complex background. To deal with this problem, firstly, this paper proposes an online discrimination dictionary learning model based on the Fisher criterion. The online discrimination dictionary learning algorithm for template updating in visual tracking is designed by using the block coordinate descent and replacing operations. Secondly, the distance between the target candidate coding coefficient and the mean of target samples coding coefficients is defined as the coefficient error. The robust visual tracking is achieved by taking the combination of the reconstruction error and the coefficient error as observation likelihood in particle filter framework. The experimental results show that the proposed method has better robustness and accuracy than the state-of-the-art trackers.-
Key words:
- Visual tracking /
- Template updating /
- Dictionary learning /
- Observation likelihood
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