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基于粒子濾波與樣本加權(quán)的壓縮跟蹤算法

張紅穎 王賽男 胡文博

張紅穎, 王賽男, 胡文博. 基于粒子濾波與樣本加權(quán)的壓縮跟蹤算法[J]. 電子與信息學(xué)報(bào), 2018, 40(6): 1397-1403. doi: 10.11999/JEIT170854
引用本文: 張紅穎, 王賽男, 胡文博. 基于粒子濾波與樣本加權(quán)的壓縮跟蹤算法[J]. 電子與信息學(xué)報(bào), 2018, 40(6): 1397-1403. doi: 10.11999/JEIT170854
ZHANG Hongying, WANG Sainan, HU Wenbo. Compressive Tracking Algorithm Based on Particle Filter and Sample Weighting[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1397-1403. doi: 10.11999/JEIT170854
Citation: ZHANG Hongying, WANG Sainan, HU Wenbo. Compressive Tracking Algorithm Based on Particle Filter and Sample Weighting[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1397-1403. doi: 10.11999/JEIT170854

基于粒子濾波與樣本加權(quán)的壓縮跟蹤算法

doi: 10.11999/JEIT170854
基金項(xiàng)目: 

天津市自然科學(xué)基金青年基金(12JCQNJC00600),中央高校基本科研業(yè)務(wù)費(fèi)(3122015C016),國家自然科學(xué)基金民航聯(lián)合研究基金(U1533203)

Compressive Tracking Algorithm Based on Particle Filter and Sample Weighting

Funds: 

The Natural Science Foundation of Tianjin (12JCQNJC00600), The Fundamental Research Funds for the Central Universities (3122015C016), The National Natural Science Foundation of China (U1533203)

  • 摘要: 該文針對壓縮跟蹤算法無法適應(yīng)目標(biāo)尺度的變化以及沒有考慮樣本權(quán)重的問題,提出一種基于粒子濾波與樣本加權(quán)的壓縮跟蹤算法。首先,對壓縮特征進(jìn)行改進(jìn),提取歸一化矩形特征用于構(gòu)建目標(biāo)表觀模型。然后,引入樣本加權(quán)的思想,根據(jù)正樣本與目標(biāo)之間距離的不同賦予正樣本不同的權(quán)重,提高分類器的分類精度。最后,在粒子濾波的框架下融合尺度不變壓縮特征進(jìn)行動態(tài)狀態(tài)估計(jì),在粒子預(yù)測階段利用2階自回歸模型對粒子狀態(tài)進(jìn)行估計(jì)與預(yù)測,借助觀測模型對粒子狀態(tài)進(jìn)行更新,并且對粒子進(jìn)行重采樣以防止粒子退化。實(shí)驗(yàn)結(jié)果表明,相比于原始壓縮跟蹤算法,改進(jìn)算法能夠更好地跟蹤目標(biāo)尺度的變化,提高跟蹤的穩(wěn)定性和準(zhǔn)確性。
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出版歷程
  • 收稿日期:  2017-09-07
  • 修回日期:  2018-01-31
  • 刊出日期:  2018-06-19

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