基于视频残差神经网络的深度步态识别  

Deep Gait Recognition Based on Video Residual Neural Network

作　　者：马玉祥 代雪晶[1] MA Yu-Xiang;DAI Xue-Jing(School of Public Security Information Technology and Intelligence,Criminal Investigation Police University of China,Shenyang 110854,China)

机构地区：[1]中国刑事警察学院公安信息技术与情报学院,沈阳110854

出　　处：《计算机系统应用》2024年第4期279-287,共9页Computer Systems & Applications

基　　金：公安部科技强警基础工作专项(2016GABJC06);中央高校基本科研业务费(D2023001)。

摘　　要：步态识别是根据人体的行走方式进行身份识别.目前,大多数步态识别方法通过浅层神经网络进行特征提取,在室内步态数据集表现良好,然而在近年新公布的室外步态数据集中性能表现不佳.为了解决室外步态数据集带来的严峻挑战,提出了一种基于视频残差神经网络的深度步态识别模型.在特征提取阶段,基于提出的视频残差块构建深层3D卷积神经网络(3D CNN),提取整个步态序列的时空动力学特征;然后,引入时序池化和水平金字塔映射降低采样特征分辨率并提取局部步态特征;使用联合损失函数驱动训练过程,最后通过BNNeck平衡损失函数并调整特征空间.实验分别在公开的室内(CASIA-B)、室外(GREW、Gait3D)这3个步态数据集上进行.实验结果表明,该模型在室外步态数据集中的准确率以及收敛速度优于其他模型.Gait recognition is the process of identifying individuals based on their walking patterns.Currently,most gait recognition methods employ shallow neural networks for feature extraction,which performs well in indoor gait datasets but produces poor performance on the newly released outdoor gait datasets.To address the complicated challenges that arise from outdoor gait datasets,this study proposes a deep gait recognition model based on video residual neural networks.In the feature extraction phase,a deep 3D convolutional neural network(3D CNN)is constructed by the proposed video residual blocks to extract the spatio-temporal dynamics features of the entire gait sequence.Subsequently,temporal pooling and horizontal pyramid mapping are introduced to reduce the feature resolution of sampling data and extract local gait features.The training process is driven by a joint loss function,and finally loss functions are balanced and the feature space is adjusted by BNNeck.The experiments are conducted on three publicly available gait datasets,including both indoor(CASIA-B)and outdoor(GREW,Gait3D)gait datasets.The experimental results verify that the model outperforms other models in accuracy and convergence speed on outdoor gait datasets.

关 键 词：计算机视觉 步态识别 视频残差神经网络 金字塔映射 深度学习 步态轮廓图像 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]