基于显著性多尺度特征协作融合的行人重识别方法

由于背景信息复杂、遮挡等因素的影响,现有基于局部特征的行人重识别方法所提取的特征不具有辨别力和鲁棒性,从而导致重识别精度较低,针对该问题,提出一种基于显著性检测与多尺度特征协作融合的SMC-ReID方法.利用显著性检测提取行人中具有判别力的特征区域,融合显著性特征与全局特征并完成不同尺度的切块,将上述不同尺度的特征进行...

Person Re-Identification Method Based on Joint Fusion of Saliency Multi-Scale Features

The existing person re-identification methods are limited by multiple factors, such as complex background information and occlusion, which reduces the discrimination and robustness of extracted features, leading to a low re-identification accuracy.To address the problem, this paper proposes a new method called SMC-ReID based on saliency detection and collaborative fusion of multi-scale features.The method employs saliency detection to extract discriminative feature areas in pedestrians, and the saliency features are fused with global features.Then the features are cut at different scales, and collaboratively fused to ensure the continuity of the cut features.Finally, the three loss functions are combined to learn based on the differences between global and local features.In the inference stage, the features of each scale are reduced to the same dimension, and fused into new feature vectors for similarity measurement. Experimental results on the public datasets for person re-identification, such as Market1501, DukeMTMC-reID and CUHK03, show that the features extracted by the proposed method have strong distinguishability and robustness, and the method has higher identification accuracy than SVDNet, PSE+ECN and other advanced algorithms.