RealGait: Gait Recognition for Person Re-Identification
- URL: http://arxiv.org/abs/2201.04806v1
- Date: Thu, 13 Jan 2022 06:30:56 GMT
- Title: RealGait: Gait Recognition for Person Re-Identification
- Authors: Shaoxiong Zhang, Yunhong Wang, Tianrui Chai, Annan Li, Anil K. Jain
- Abstract summary: 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.
Our results suggest that recognizing people by their gait in real surveillance scenarios is feasible and the underlying gait pattern is probably the true reason why video person re-idenfification works in practice.
- Score: 79.67088297584762
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 a novel gait recognition method, called
RealGait. Our results suggest that recognizing people by their gait in real
surveillance scenarios is feasible and the underlying gait pattern is probably
the true reason why video person re-idenfification works in practice.
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