基于笛卡爾乘積字典的稀疏編碼跟蹤算法
doi: 10.11999/JEIT140931 cstr: 32379.14.JEIT140931
基金項目:
國家自然科學(xué)基金(61175029, 61379104, 61372167)，國家自然科學(xué)基金青年科學(xué)基金(61203268, 61202339)，博士后特別資助基金(2012M512144)和博士后面上資助基金(2012JQ8034)資助課題
Sparse Coding Visual Tracking Based on the Cartesian Product of Codebook
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摘要: 為了提高基于稀疏編碼的視頻目標(biāo)跟蹤算法的魯棒性，該文將原始稀疏編碼問題分解為兩個子稀疏編碼問題，在大大增加字典原子個數(shù)的同時，降低了稀疏性求解過程的計算量。并且為了減少1范數(shù)最小化的計算次數(shù)，利用基于嶺回歸的重構(gòu)誤差先對候選目標(biāo)進行粗估計，而后選取重構(gòu)誤差較小的若干個粒子求解其在兩個子字典下的稀疏表示，最后將目標(biāo)的高維稀疏表示代入事先訓(xùn)練好的分類器，選取分類器響應(yīng)最大的候選位置作為目標(biāo)的跟蹤位置。實驗結(jié)果表明由于笛卡爾乘積字典的應(yīng)用使得算法的魯棒性得到一定程度的提高。Abstract: In order to improve the robustness of the visual tracking algorithm based on sparse coding, the original sparse coding problem is decomposed into two sub sparse coding problems. And the size of the codebook is intensively increased while the computational cost is decreased. Furthermore, in order to decrease the number of the1-norm minimization, ridge regression is employed to exclude the intensive outlying particles via the reconstruction error. And the sparse representation of the particles with small reconstruction error is computed on the two subcodebooks. The high-dimension sparse representation is put into the classifier and the candidate with the biggest response is recognized as the target. The experiment results demonstrate that the robustness of the proposed algorithm is improved due to the employed Cartesian product of subcodebooks.
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