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基于感知深度神經(jīng)網(wǎng)絡(luò)的視覺(jué)跟蹤

侯志強(qiáng) 戴鉑 胡丹 余旺盛 陳晨 范舜奕

侯志強(qiáng), 戴鉑, 胡丹, 余旺盛, 陳晨, 范舜奕. 基于感知深度神經(jīng)網(wǎng)絡(luò)的視覺(jué)跟蹤[J]. 電子與信息學(xué)報(bào), 2016, 38(7): 1616-1623. doi: 10.11999/JEIT151449
引用本文: 侯志強(qiáng), 戴鉑, 胡丹, 余旺盛, 陳晨, 范舜奕. 基于感知深度神經(jīng)網(wǎng)絡(luò)的視覺(jué)跟蹤[J]. 電子與信息學(xué)報(bào), 2016, 38(7): 1616-1623. doi: 10.11999/JEIT151449
HOU Zhiqiang, DAI Bo, HU Dan, YU Wangsheng, CHEN Chen, FAN Shunyi. Robust Visual Tracking via Perceptive Deep Neural Network[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1616-1623. doi: 10.11999/JEIT151449
Citation: HOU Zhiqiang, DAI Bo, HU Dan, YU Wangsheng, CHEN Chen, FAN Shunyi. Robust Visual Tracking via Perceptive Deep Neural Network[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1616-1623. doi: 10.11999/JEIT151449

基于感知深度神經(jīng)網(wǎng)絡(luò)的視覺(jué)跟蹤

doi: 10.11999/JEIT151449
基金項(xiàng)目: 

國(guó)家自然科學(xué)基金(61175029, 61473309),陜西省自然科學(xué)基金(2015JM6269,2015JM6269,2016JM6050)

Robust Visual Tracking via Perceptive Deep Neural Network

Funds: 

The National Natural Science Foundation of China (61175029, 61473309), The Natural Science Foundation of Shaanxi Province (2015JM6269, 2015JM6269, 2016JM6050)

  • 摘要: 視覺(jué)跟蹤系統(tǒng)中,高效的特征表達(dá)是決定跟蹤魯棒性的關(guān)鍵,而多線索融合是解決復(fù)雜跟蹤問(wèn)題的有效手段。該文首先提出一種基于多網(wǎng)絡(luò)并行、自適應(yīng)觸發(fā)的感知深度神經(jīng)網(wǎng)絡(luò);然后,建立一個(gè)基于深度學(xué)習(xí)的、多線索融合的分塊目標(biāo)模型。目標(biāo)分塊的實(shí)現(xiàn)成倍地減少了網(wǎng)絡(luò)輸入的維度,從而大幅降低了網(wǎng)絡(luò)訓(xùn)練時(shí)的計(jì)算復(fù)雜度;在跟蹤過(guò)程中,模型能夠根據(jù)各子塊的置信度動(dòng)態(tài)調(diào)整權(quán)重,提高對(duì)目標(biāo)姿態(tài)變化、光照變化、遮擋等復(fù)雜情況的適應(yīng)性。在大量的測(cè)試數(shù)據(jù)上進(jìn)行了實(shí)驗(yàn),通過(guò)對(duì)跟蹤結(jié)果進(jìn)行定性和定量分析表明,所提出算法具有很強(qiáng)的魯棒性,能夠比較穩(wěn)定地跟蹤目標(biāo)。
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出版歷程
  • 收稿日期:  2015-12-22
  • 修回日期:  2016-05-04
  • 刊出日期:  2016-07-19

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