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改進的協同訓練框架下壓縮跟蹤

鄭超 陳杰 殷松峰 楊星 馮云松 凌永順

鄭超, 陳杰, 殷松峰, 楊星, 馮云松, 凌永順. 改進的協同訓練框架下壓縮跟蹤[J]. 電子與信息學報, 2016, 38(7): 1624-1630. doi: 10.11999/JEIT151001
引用本文: 鄭超, 陳杰, 殷松峰, 楊星, 馮云松, 凌永順. 改進的協同訓練框架下壓縮跟蹤[J]. 電子與信息學報, 2016, 38(7): 1624-1630. doi: 10.11999/JEIT151001
ZHENG Chao, CHEN Jie, YIN Songfeng, YANG Xing, FENG Yunsong, LING Yongshun. Optimized Compressive Tracking in Co-training Framework[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1624-1630. doi: 10.11999/JEIT151001
Citation: ZHENG Chao, CHEN Jie, YIN Songfeng, YANG Xing, FENG Yunsong, LING Yongshun. Optimized Compressive Tracking in Co-training Framework[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1624-1630. doi: 10.11999/JEIT151001

改進的協同訓練框架下壓縮跟蹤

doi: 10.11999/JEIT151001
基金項目: 

安徽高校自然科學重大研究項目(KJ2015ZD14),國家自然科學基金(61405248, 61503394),安徽省自然科學基金(1408085 QF131, 1508085QF121)

Optimized Compressive Tracking in Co-training Framework

Funds: 

Higher Education Institutes Natural Science Research Project of Anhui Province of China (KJ2015ZD14), The National Natural Science Foundation of China (61405248, 61503394), The Natural Science Foundation of Anhui Province (1408085QF131, 1508085QF121)

  • 摘要: 針對基于傳統協同訓練框架的視覺跟蹤算法在復雜環(huán)境下魯棒性不足,該文提出一種改進的協同訓練框架下壓縮跟蹤算法。首先,利用空間布局信息,基于能量熵最大化的在線特征選擇技術提升壓縮感知分類器的判別能力,分別在灰度空間和局部二值模式空間建立起基于結構壓縮特征的兩個獨立分類器。然后,基于候選樣本信任度分布熵的分類器聯合機制實現互補性特征的自適應融合,增強跟蹤結果的魯棒性。最后,在級聯的梯度直方圖分類器輔助下,通過具備樣本選擇能力的新型協同訓練準則完成聯合外觀模型的準確更新,解決了協同訓練誤差的積累問題。對大量具有挑戰(zhàn)性的序列的對比實驗結果驗證了該算法相比于其它近似跟蹤算法具有更優(yōu)的性能。
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  • 被引次數: 0
出版歷程
  • 收稿日期:  2015-09-08
  • 修回日期:  2016-01-11
  • 刊出日期:  2016-07-19

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