Can I read RealGait: Gait Recognition for Person Re-Identification on EtoBox?

RealGait: Gait Recognition for Person Re-Identification by Zhang, Shaoxiong; Wang, Yunhong; Chai, Tianrui; Li, Annan; Jain, Anil K. is a scholarly article available to read on EtoBox.

What is RealGait: Gait Recognition for Person Re-Identification about?

Human gait is considered a unique biometric identifier which can be acquired in a covert manner at a distance. However, models trained on existing public domain gait datasets which are captured in controlled scenarios lead to drastic performance decline when applied to real-world unconstrained gait data. On the other hand, video person re-identification techniques have achieved promising performance on large-scale publicly available datasets. Given the diversity of clothing characteristics, clothing cue is not reliable for person recognition in general. So, it is actually not clear why the state-of-the-art person re-identification methods work as well as they do. In this paper, we construct a new gait dataset by extracting silhouettes from an existing video person re-identification challenge which consists of 1,404 persons walking in an unconstrained manner. Based on this dataset, a consistent and comparative study between gait recognition and person re-identification can be carried out. Given that our experimental results show that current gait recognition approaches designed under data collected in controlled scenarios are inappropriate for real surveillance scenarios, we propose

Author

Zhang, Shaoxiong; Wang, Yunhong; Chai, Tianrui; Li, Annan; Jain, Anil K.

Published

2022

Language

EN