基于典型相關(guān)性分析的稀疏表示目標(biāo)追蹤

康彬; 曹雯雯; 顏俊; 張索非

doi:10.11999/JEIT170939

基于典型相關(guān)性分析的稀疏表示目標(biāo)追蹤

doi: 10.11999/JEIT170939 cstr: 32379.14.JEIT170939

^①(南京郵電大學(xué)物聯(lián)網(wǎng)學(xué)院南京 210003) ^②(南京郵電大學(xué)江蘇省通信與網(wǎng)絡(luò)技術(shù)工程研究中心南京 210003) ^③(南京郵電大學(xué)通信與信息工程學(xué)院南京 210003)

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61771256, 61471205, 61771258, 61701252)，江蘇省自然科學(xué)基金青年基金(BK20170915)，江蘇省高校自然科學(xué)面上項(xiàng)目(17KJD510005)，南京郵電大學(xué)引進(jìn)人才啟動(dòng)基金(NY 216023)，南京郵電大學(xué)江蘇省通信與網(wǎng)絡(luò)技術(shù)工程研究中心開放課題

詳細(xì)信息

作者簡(jiǎn)介:
康彬：康彬：男，1985年生，講師，研究方向?yàn)橄∈璞硎纠碚摗⒛繕?biāo)檢測(cè)以及追蹤等. 曹雯雯：女，1993年生，碩士生，研究方向?yàn)橹悄苄盘?hào)處理. 顏 ?。? 男，1981年生，副教授，研究方向?yàn)橹悄苄盘?hào)處理. 張索非：男，1982年生，講師，研究方向?yàn)槟繕?biāo)檢測(cè)以及追蹤.

中圖分類號(hào): TP391
計(jì)量
- 文章訪問(wèn)數(shù): 1657
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-10-11
- 修回日期: 2018-03-14
- 刊出日期: 2018-07-19

Canonical Correlation Analysis Based Sparse Representation Model for Robust Visual Tracking

KANG Bin^①② CAO Wenwen^③ YAN Jun^③ ZHANG Suofei^①

Funds:

The National Natural Science Foundation of China (61771256, 61471205, 61771258, 61701252), The Natural Science Foundation of Jiangsu Province (BK20170915), The Natural Science Foundation of Jiangsu Higher Education Institutions (17KJD510005), The Nanjing University of Posts and Telecommunications Program (NY 216023), Supported by Open Research Fund of Jiangsu Engineering Research Center of Communication and Network Technology, Nanjing University of Posts and Telecommunications

摘要

摘要: 傳統(tǒng)稀疏表示目標(biāo)追蹤算法首先通過(guò)粒子濾波方法對(duì)狀態(tài)粒子進(jìn)行采樣，然后利用灰度特征表征采樣粒子觀測(cè)向量，最后構(gòu)造基于觀測(cè)向量的稀疏表示模型來(lái)進(jìn)行目標(biāo)追蹤。與傳統(tǒng)稀疏表示模型不同，該文提出一個(gè)基于典型相關(guān)性分析的稀疏表示模型，此模型首先使用兩種特征來(lái)表征粒子觀測(cè)向量，然后對(duì)兩種觀測(cè)向量的子空間投影結(jié)果進(jìn)行稀疏建模。所構(gòu)建的模型可通過(guò)在子空間中探究特征間的相關(guān)性來(lái)實(shí)現(xiàn)不同特征的互補(bǔ)融合，提升稀疏表示模型在復(fù)雜監(jiān)控環(huán)境下的魯棒性。
- 目標(biāo)追蹤 /
- 稀疏表示 /
- 典型相關(guān)性分析
Abstract: In traditional sparse representation based visual tracking, particle sampling is first achieved by particle filter method. Then the particle observations are represented by intensity feature. Finally, the visual tracking is achieved by the intensity feature based sparse representation model. Different from traditional sparse representation model, a canonical correlation analysis based sparse representation model is proposed in this paper. The proposed model first uses two kinds of features to represent the particle observations, then, the projections of particle observations are used to build the sparse representation model. The advantage of the proposed model lies in that it can give a proper multi-feature fusing through canonical correlation analysis, which explores the relation between two features in a latent common subspace.
- Visual tracking、Sparse representation、Canonical correlation analysis /

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參考文獻(xiàn)(24)

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