摘要
针对非重叠视角下的行人重识别和高维特征提取等问题,提出基于块稀疏表示的行人重识别方法。采取典型相关分析(CCA)方法进行特征投影变换,通过提高特征匹配能力来避免高维特征运算引起的维数灾难问题,并在CCA转换后的投影空间使投影后查询集行人特征向量与相应的数据集特征向量近似成线性关系;利用行人数据集的块结构特征构建行人重识别模型,采用交替方向框架求解优化问题;最后对查询集中要识别的行人采用残差项处理,并将最小残差项所对应的指标作为最终识别的行人记号。在公开数据集PRID 2011、iLIDS-VID和VIPeR上进行多次实验,结果显示所提方法的Rank1性能在三个数据集上分别达到40.4%、38.11%和23.68%,明显高于大间隔最近邻分类(LMNN)等算法,其在Rank-1上的匹配率也远大于LMNN算法;其总体性能也优于经典的基于特征表示与度量学习的算法。实验结果验证了所提方法在行人重识别上的有效性。
Focusing on the person re-identification in non-overlapping camera views and the high dimensional feature extracted from the images, a person re-identification method based on block sparse representation was proposed. The Canonical Correlation Analysis( CCA) was taken to carry out the feature projection transformation, and the curse of dimensionality caused by high dimensional feature operation was avoided by improving the feature matching ability, and the feature vectors in a probe image were made to be probably linear with the corresponding gallery feature vectors in the learned projected space of CCA transformation. A person re-identification model was also built with block structure feature of pedestrian dataset, and the associated optimization problem was solved by utilizing the alternating direction framework.Finally, the residues were used to deal with the person in the probe set to be identified and the index of the minimum value in the residues was regarded as the identity of the person. Several experiments were conducted on public datasets such as PRID2011, iLIDS-VID and VIPeR. The experimental results show that the Rank1 value of the proposed method on three experimental datasets reaches 40. 4%, 38. 11% and 23. 68%, respectively, which is significantly higher than that of Large Margin Nearest Neighbor( LMNN) method, and the matching rate of it on Rank-1 is also much bigger than that of LMNN method; besides, the overall performance of it is better than the classical algorithms based on feature representation and metric learning. The experimental results verify the effectiveness of the proposed method on person re-identification.
出处
《计算机应用》
CSCD
北大核心
2018年第2期448-453,共6页
journal of Computer Applications
基金
国家自然科学基金资助项目(61572085)
江苏省产学研前瞻性联合研究项目(BY2016029-15)~~
关键词
行人重识别
投影空间
块稀疏
交替方向框架
person re-identification
projected space
block sparsity
alternating direction framework
