ReID2.0:从行人再识别走向人像态势计算  

ReID2.0:from person ReID to portrait interpretation

作　　者：王生进[1,2] 豆朝鹏 樊懿轩 李亚利 Wang Shengjin;Dou Zhaopeng;Fan Yixuan;Li Yali(Department of Electronic Engineering,Tsinghua University,Beijing 100084,China;Beijing National Research Center of Information Science and Technology,Beijing 100084,China)

机构地区：[1]清华大学电子工程系,北京100084 [2]北京信息科学与技术国家研究中心,北京100084

出　　处：《中国图象图形学报》2023年第5期1326-1345,共20页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61071135,61701277,61771288)。

摘　　要：行人再识别(person re-identification,Person ReID)指利用计算机视觉技术对在一个摄像头的视频图像中出现的某个确定行人在其他时间、不同位置的摄像头中再次出现时能够辨识出来,或在图像或视频库中检索特定行人。行人再识别研究具有强烈的实际需求,在公共安全、新零售以及人机交互领域具有潜在应用,具备显著的机器学习和计算机视觉领域的理论研究价值。行人成像存在复杂的姿态、视角、光照和成像质量等变化,同时也有一定范围的遮挡等难点,因此行人再识别面临着非常大的技术挑战。近年来,学术界和产业界投入了巨大的人力和资源研究该问题,并取得了一定进展,在多个数据集上的平均准确率均值(mean average precision,mAP)有了较大提升,并部分开始实际应用。尽管如此,当前行人再识别研究主要还是侧重于服装表观的特征,缺乏对行人表观显式的多视角观测和描述,这与人类观测的机理不尽相符。本文旨在打破现有行人再识别任务的设定,形成对行人综合性观测描述。为推进行人再识别研究的进展,本文在前期行人再识别研究的基础上提出了人像态势计算的概念(ReID2.0)。人像态势计算以像态、形态、神态和意态这4态对人像的静态属性和似动状态进行多视角观测和描述。构建了一个新的基准数据集Portrait250K,包含250000幅人像和对应8个子任务的手动标记的8种标签,并提出一个新的评价指标。提出的人像态势计算从多视角表观信息对行人形成综合性的观测描述,为行人再识别2.0以及类人智能体的进一步研究提供了参考。Person re-identification(Person ReID)has been concerned more in computer vision nowadays.It can identify a pedestrian-targeted in the images and recognize its multiple spatio-temporal re-appearance.Person ReID can be used to retrieve pedestrians-specific from image or video databases as well.Person re-identification research has strong practical needs and has potential applications in the fields of public safety,new retailing,and human-computer interaction.Conventional forensic-based human-relevant face recognition can provide one of the most powerful technical means for identity checking.However,it is challenged that imaging-coordinated is restricted by its rigid angle and distance.The semicoordinated face recognition is evolved in technically.Actually,there are a large number of scenarios-discreted to be dealt with for public surveillance,where the monitored objects do not need to cooperate with the camera to image,and they do not need to be aware that they are being filmed;in some extreme cases,Some suspects may even deliberately cover themselves key biometric features.To provide wide-ranged tracking spatiotemepally,the surveillance of public security is called for person re-identification urgently.It is possible to sort facial elements out from the back and interprete the facial features further in support of pedestrian re-identification technology.The potential of the person re-identification task is that the recognition object is a non-cooperative target.Pedestrian-oriented imaging has challenged for complicated changes in relevant to its posture,viewing angle,illumination,imaging quality,and certain occlusion-ranged.The key challenges are dealt with its learning-related issues of temporal-based image feature expression and spatial-based meta-image data to the distinctive feature.In addition,compared to the face recognition task,data collection and labeling are more challenging in the person re-identification task,and existing datasets gap are called to be bridged and richer intensively in comparison with f

关 键 词：行人再识别 人像态势计算 ReID2.0 表征学习 计算机视觉 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]