基于多池化融合与背景消除网络的跨数据集行人再识别方法

Cross-dataset person re-identification method based on multi-pool fusion and background elimination network

现有跨数据集行人再识别方法一般致力于减小2个数据集之间的数据分布差异,忽略了背景信息对识别性能的影响。针对上述问题,提出了一种基于多池化融合与背景消除网络的跨数据集行人再识别方法。为了兼顾全局特征和局部特征,同时实现特征的多细粒度表示,构建了多池化融合网络。为了使监督网络能提取有用的行人前景特征,构建了特征级有监督背景消除网络。采用结合行人分类损失及特征激活损失的多任务学习损失函数,在3个公开行人再识别数据集上对方法进行评估,当MSMT17作为训练集时,Market-1501上的跨数据集识别性能mAP为35.53%,相比ResNet50网络提升了9.24%;DukeMTMC-reID上的跨数据集识别性能m AP为41.45%,相比于ResNet50网络提升了10.72%。与现有方法相比,所提方法具有更优的跨数据集行人再识别性能。

The existing cross-dataset person re-identification methods were generally aimed at reducing the difference of data distribution between two datasets,which ignored the influence of background information on recognition performance.In order to solve this problem,a cross-dataset person re-ID method based on multi-pool fusion and background elimination network was proposed.To describe both global and local features and implement multiple fine-grained representations,a multi-pool fusion network was constructed.To supervise the network to extract useful foreground features,a feature-level supervised background elimination network was constructed.The final network loss function was defined as a multi-task loss,which combined both person classification loss and feature activation loss.Three person re-ID benchmarks were employed to evaluate the proposed method.Using MSMT17 as the training set,the cross-dataset m AP for Market-1501 was 35.53%,which was 9.24%higher than ResNet50.Using MSMT17 as the training set,the cross-dataset m AP for DukeMTMC-reID was 41.45%,which was 10.72%higher than ResNet50.Compared with existing methods,the proposed method shows better cross-dataset person re-ID performance.