基于深度多视图特征距离学习的行人重识别

Person re-identification based on deep multi-view feature distance learning

传统手工特征很大程度上依赖于行人的外观特征,而深度卷积特征作为高维特征,直接用来匹配图像会消耗大量的时间和内存,并且来自较高层的特征很容易受到行人姿势背景杂波影响。针对这些问题,提出一种基于深度多视图特征距离学习的方法。首先,提出一种新的整合和改善深度区域的卷积特征,利用滑框技术对卷积特征进行处理,得到低维的深度区域聚合特征并使其维数等于卷积层通道数;其次,通过交叉视图二次判别分析方法,从深度区域聚合特征和手工特征两个角度出发,提出一种多视图特征距离学习算法;最后,利用加权融合策略来完成传统特征和卷积特征之间的协作。在Market-1501和VIPeR数据集上的实验结果显示,所提融合模型的Rank1值在两个数据集上分别达到80.17%和75.32%;在CUHK03数据集新分类规则下,所提方法的Rank1值达到33.5%。实验结果表明,通过距离加权融合之后的行人重识别的精度明显高于单独的特征距离度量取得的精度,验证了所提的深度区域特征和算法模型的有效性。

The traditional handcrafted features rely heavily on the appearance characteristics of pedestrians and the deep convolution feature is a high-dimensional feature,so,it will consume a lot of time and memory when the feature is directly used to match the image.Moreover,features from higher levels are easily affected by human pose or background clutter.Aiming at these problems,a method based on deep multi-view feature distance learning was proposed.Firstly,a new feature to improve and integrate the convolution feature of the deep region was proposed.The convolution feature was processed by the sliding frame technique,and the integration feature of low-dimensional deep region with the dimension equal to the number of convolution layer channels was obtained.Secondly,from the perspectives of the deep regional integration feature and the handcrafted feature,a multi-view feature distance learning algorithm was proposed by utilizing the cross-view quadratic discriminant analysis method.Finally,the weighted fusion strategy was used to accomplish the collaboration between handcrafted features and deep convolution features.Experimental results show that the Rank1 value of the proposed method reaches 80.17% and 75.32% respectively on the Market-1501 and VIPeR datasets;under the new classification rules of CHUK03 dataset,the Rank1 value of the proposed method reaches 33.5%.The results show that the accuracy of pedestrian re-identification after distance-weighted fusion is significantly higher than that of the separate feature distance metric,and the effectiveness of the proposed deep region features and algorithm model are proved.