论文标题
通过轻量级卷积神经网络改进的人重新识别方法
An Improved Person Re-identification Method by light-weight convolutional neural network
论文作者
论文摘要
人重新识别被定义为一个识别过程，在不同地方，通过非重叠的摄像机观察到该人。在过去的十年中，人重新识别监视系统的应用和重要性的上升和重要性在计算机视觉的不同领域中普及了这一主题。人重新识别面临着诸如低分辨率，不同姿势，照明，背景混乱和遮挡等挑战，这可能会影响识别过程的结果。本文旨在通过在暹罗网络框架内使用转移学习和应用验证损失功能的应用程序来改善人员重新识别。暹罗网络接收图像对作为输入，并通过预先训练的模型提取其功能。使用EditiveNet来获得判别特征并减少对数据的需求。网络学习使用了验证损失的优势。实验表明，所提出的模型的性能优于Cuhk01数据集上的最新方法。例如，对于CUHK01数据集，Rank5精度为95.2％（+5.7）。它在等级1中也达到了可接受的百分比。由于预先训练的模型参数的尺寸很小，学习速度会加快，并且需要更少的硬件和数据。
Person Re-identification is defined as a recognizing process where the person is observed by non-overlapping cameras at different places. In the last decade, the rise in the applications and importance of Person Re-identification for surveillance systems popularized this subject in different areas of computer vision. Person Re-identification is faced with challenges such as low resolution, varying poses, illumination, background clutter, and occlusion, which could affect the result of recognizing process. The present paper aims to improve Person Re-identification using transfer learning and application of verification loss function within the framework of Siamese network. The Siamese network receives image pairs as inputs and extract their features via a pre-trained model. EfficientNet was employed to obtain discriminative features and reduce the demands for data. The advantages of verification loss were used in the network learning. Experiments showed that the proposed model performs better than state-of-the-art methods on the CUHK01 dataset. For example, rank5 accuracies are 95.2% (+5.7) for the CUHK01 datasets. It also achieved an acceptable percentage in Rank 1. Because of the small size of the pre-trained model parameters, learning speeds up and there will be a need for less hardware and data.
