视频监控场景下基于单视角步态的人体身份及属性识别系统

基于步态的特征识别是一种新兴的生物特征识别技术,旨在通过人们走路的姿态进行身份和相关属性的分析.与其他的生物识别技术相比,基于步态的识别方法具有难隐藏性、非接触性和可远距离使用的优点.本文设计出一个视频监控场景下基于单视角步态的人体身份及属性识别系统,该系统通过图像处理方法从复杂的监控视频中实时检测出人体的步态,经过利用深度学习训练过的算法进行分析后,获取人体的身份、性别和年龄信息.实验表明,系统的身份识别准确率达98.1%,性别预测准确率达97.1%,年龄预测平均绝对误差为6.21岁,实验结果均优于传统基准算法,且系统开发成本低,支持实时检测,能充分满足中小规模步态研究与分析的需要.

Single-View Gait-Based Identity and Attributes Recognition System under Video Surveillance

Gait-based feature recognition is an emerging biometric authentication technology, aiming at analyzing human characteristics such as identity through the walking posture of people. Compared with other biological recognition technologies, gait-based methods have the advantages of being difficult to hide, contactless, and remotely usable. This study designs a single-view gait-based human identity and attributes recognition system under video surveillance. The system uses image processing methods to detect a human gait in real-time from a complex surveillance video. After analyzing with the algorithm trained by deep learning, it can obtain the information of human''s identity, gender, and age. Experiments show that the accuracy rate of the system is 98.1%, the accuracy of gender prediction is 97.1%, and the mean absolute error of the age prediction is 6.21, which are better than the traditional benchmark. The system is costless, supporting real-time detection, which can fully meet the needs of small and medium-scale gait research and analysis.