Pedestrian reidentification is a key technology in large-scale distributed camera systems. It can quickly and efficiently detect and track target people in large-scale distributed surveillance networks. The existing traditional pedestrian reidentification methods have problems such as low recognition accuracy, low calculation efficiency, and weak adaptive ability. Pedestrian reidentification algorithms based on deep learning have been widely used in the field of pedestrian reidentification due to their strong adaptive ability and high recognition accuracy. However, the pedestrian recognition method based on deep learning has the following problems: first, during the learning process of the deep learning model, the initial value of the convolution kernel is usually randomly assigned, which makes the model learning process easily fall into a local optimum. The second is that the model parameter learning method based on the gradient descent method exhibits gradient dispersion. The third is that the information transfer of pedestrian reidentification sequence images is not considered. In view of these issues, this paper first examines the feature map matrix from the original image through a deconvolution neural network, uses it as a convolution kernel, and then performs layer-by-layer convolution and pooling operations. Then, the second derivative information of the error function is directly obtained without calculating the Hessian matrix, and the momentum coefficient is used to improve the convergence of the backpropagation, thereby suppressing the gradient dispersion phenomenon. At the same time, to solve the problem of information transfer of pedestrian reidentification sequence images, this paper proposes a memory network model based on a multilayer attention mechanism, which uses the network to effectively store image visual information and pedestrian behavior information, respectively. It can solve the problem of information transmission. Based on the above ideas, this paper proposes a pedestrian reidentification algorithm based on deconvolution network feature extraction-multilayer attention mechanism convolutional neural network. Experiments are performed on the related data sets using this algorithm and other major popular human reidentification algorithms. The results show that the pedestrian reidentification method proposed in this paper not only has strong adaptive ability but also has significantly improved average recognition accuracy and rank-1 matching rate compared with other mainstream methods.

中文翻译：

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基于反卷积网络特征提取-多层注意机制卷积神经网络的行人识别算法

行人识别是大型分布式摄像头系统中的一项关键技术。它可以在大型分布式监视网络中快速有效地检测和跟踪目标人员。现有的传统行人识别方法存在识别精度低，计算效率低，自适应能力差等问题。基于深度学习的行人识别算法由于具有较强的自适应能力和较高的识别精度，已经在行人识别领域得到了广泛的应用。然而，基于深度学习的行人识别方法存在以下问题：首先，在深度学习模型的学习过程中，卷积核的初始值通常是随机分配的，这使得模型学习过程很容易陷入局部最优状态。第二是基于梯度下降法的模型参数学习方法表现出梯度色散。第三是不考虑行人识别序列图像的信息传递。针对这些问题，本文首先通过反卷积神经网络检查原始图像的特征图矩阵，将其用作卷积核，然后进行逐层卷积和池化操作。然后，不计算Hessian矩阵而直接获得误差函数的二阶导数信息，并且动量系数用于改善反向传播的收敛性，从而抑制梯度色散现象。与此同时，为了解决行人识别序列图像的信息传递问题，提出了一种基于多层注意机制的存储网络模型，该模型利用网络分别有效地存储了图像视觉信息和行人行为信息。它可以解决信息传输的问题。基于以上思想，提出了一种基于反卷积网络特征提取-多层注意力机制卷积神经网络的行人识别算法。使用此算法和其他主要的流行人类重新识别算法对相关数据集进行了实验。