面向多人多生物属性的跨视角步态追踪系统

Cross-view Gait Tracking System Oriented to Multiple People and Multiple Biological Attributes

步态识别是一项新兴的生物识别技术,可以被广泛地应用在刑事安防,疫情传播链追踪等领域,该项技术的本质在于通过人的人体体型和行走姿态来识别人的身份,年龄,性别等多种生物属性.相比其他生物识别技术,步态识别具有远距离,全视角,无感知,防伪装等显著优势.基于此,本文设计了一款面向多人多生物属性的跨视角步态追踪系统,该系统充分考虑了现实应用场景中存在的多人,跨视角,服饰变化等协变量对于步态识别准确率的影响,并通过更加鲁棒的算法设计从复杂的环境中提取行人的步态信息从而对其身份,年龄,性别等生物属性进行准确的分析.实验结果表明,在跨视角和多种行走状态的情况下,本系统中基于深度学习的步态识别算法模型的准确率可以达到88.0%,在多视角的情况下,性别分类准确率可以达到94.8%,年龄估计的平均年龄误差约为7.92岁,标准差约为8.11,实验结果均优于近年来相关领域的算法,达到相对领先的水平.同时系统开发成本低,面向落地应用场景,并支持实时性步态检测.

Gait recognition is an emerging biometric technology,which can be widely used in criminal security,epidemic transmission chain tracking,etc.The essence of this technology is to identify people’s identity,age,gender and other biological attributes through their human body shape and walking posture.Compared with other biometric technologies,gait recognition has significant advantages such as long distance,full view,no perception,and anti-counterfeiting.In this study,we design a cross-view gait tracking system for multiple people and multiple biological attributes.The system fully considers the impact of covariates(such as multiple people,cross view and clothing change)on gait recognition accuracy in real application scenarios.It extracts the gait information of pedestrians from complex environments to accurately analyze their biological attributes such as identity,age,and gender through a more robust algorithm design.The experimental results show that the accuracy of the deep learning-based gait recognition algorithm model in this system can reach 88.0%in the case of cross view and multiple walking states and 94.8%in the case of multiple views for gender classification.The average age error of age estimation is about 7.92 years with a standard deviation of about 8.11.These results are better than those of recent algorithms in related fields and reach a relatively leading level.At a low development cost,the system is oriented to application scenarios and supports real-time gait detection.