Person reidentification (ReID) requires the discriminative features of an entire pedestrian image to handle the problems of inaccurate person bounding box detection, background confusion, and occlusion. Many recent person ReID methods have attempted to learn the feature information of an entire pedestrian image through parts feature representations, but it is often too time consuming. Person ReID relies on discriminative pedestrian features, and different spatial scales can distinguish features by differing degrees. We propose an innovative and effective adaptive spatial scale person ReID network model based on the residual neural network (ResNet) of an instance batch normalization. Through experimental visualizations, pedestrian features extracted by ResNet from four layers are analyzed, and two layers with discriminative features are selected. Using an adaptive dimension adjustment module, different spatial scale features are aggregated and merged by the aggregation layer. To effectively learn spatial channel correlations and prevent overfitting, a multilayer distribution normalization processing module is designed to implement end-to-end training and evaluate the person ReID networks. Compared with other methods, this network model showed excellent performance on four public person ReID datasets and is superior to most current methods.

中文翻译：

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自适应空间尺度人识别

人员识别（ReID）需要整个行人图像的区分特征来处理不正确的人员边界框检测，背景混乱和遮挡的问题。许多最近的人ReID方法已经尝试通过部分特征表示来学习整个行人图像的特征信息，但是这通常太耗时。Person ReID依赖于可区分的行人特征，并且不同的空间比例可以按不同的程度区分特征。我们基于实例批归一化的残差神经网络（ResNet），提出了一种创新，有效的自适应空间尺度人ReID网络模型。通过实验可视化，分析了ResNet从四层提取的行人特征，并选择了具有区分性特征的两层。使用自适应尺寸调整模块，聚合层可以聚合和合并不同的空间比例尺特征。为了有效地学习空间信道相关性并防止过度拟合，设计了多层分布规范化处理模块来实施端到端训练并评估人ReID网络。与其他方法相比，该网络模型在四个公众ReID数据集上表现出出色的性能，并且优于大多数当前方法。