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

doi:10.11999/JEIT151001

改進的協同訓練框架下壓縮跟蹤

doi: 10.11999/JEIT151001

1.
(電子工程學院脈沖功率激光技術國家重點實驗室合肥 230037) ②(安徽建筑大學電子與信息工程學院合肥 230037)

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)

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- 文章訪問數: 1426
- HTML全文瀏覽量: 133
- PDF下載量: 401
- 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

Optimized Compressive Tracking in Co-training Framework

1.
(State Key Laboratory of Pulsed Power Laser Technology, Electronic Engineering Institute, Hefei 230037, China)
2.
(Electronics and Information Engineering Institute, Anhui Jianzhu University, Hefei 230037, China)

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

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。
- 視覺跟蹤 /
- 協同訓練 /
- 空間布局信息 /
- 壓縮感知分類器 /
- 熵
Abstract: As visual tracking algorithms based on traditional co-training framework are characterized by poor robustness in complex environment, an optimized compressive tracking algorithm in a novel co-training criterion is proposed. Firstly, the spatial layout information and the online feature selection technique based on maximizing entropy energy are used to improve the discriminative capacity of compressive sense classifier, and two independent classifiers are constructed by structural compressive features selected from the gray intensity space and the local binary pattern space respectively. Secondly, on the basis of the classifiers collaborative strategy that is acquired by calculating the confidence score distribution entropy of the candidate samples, complementary features can be adaptive fused, which reinforces the robustness of tracking results. Thirdly, as assistant of the cascaded Histograms of Orientation Gradient (HOG) classifier, the collaborative appearance model is updated with accuracy by a novel co-training criterion with sample selecting ability, which solves the updating error of co-training accumulation problem. Comparative experiment results on extensive challenging sequences demonstrate that the proposed algorithm is of better performance than other similar tracking algorithms.
- Visual tracking /
- Co-training /
- Spatial layout information /
- Compressive sense classifier /
- Entropy

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參考文獻(23)

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