ReSGait: The Real-Scene Gait Dataset

Mu，Zihao1; Castro，Francisco M.2; Marin-Jimenez，Manuel J.3; Guil，Nicolas4; Li，Yan Ran1; Yu，Shiqi4

10.1109/IJCB52358.2021.9484347

2021-08-04

2474-9680

978-1-6654-3781-3

2021 IEEE International Joint Conference on Biometrics, IJCB 2021

1-8

4-7 Aug. 2021

Shenzhen, China

Many studies have shown that gait recognition can be used to identify humans at a long distance, with promising results on current datasets. However, those datasets are collected under controlled situations and predefined conditions, which limits the extrapolation of the results to unconstrained situations in which the subjects walk freely in scenes. To cover this gap, we release a novel real-scene gait dataset (ReSGait), which is the first dataset collected in unconstrained scenarios with freely moving subjects and not controlled environmental parameters. Overall, our dataset is composed of 172 subjects and 870 video sequences, recorded over 15 months. Video sequences are labeled with gender, clothing, carrying conditions, taken walking route, and whether mobile phones were used or not. Therefore, the main characteristics of our dataset that differentiate it from other datasets are as follows: (i) uncontrolled real-life scenes and (ii) long recording time. Finally, we empirically assess the difficulty of the proposed dataset by evaluating state-of-The-Art gait approaches for silhouette and pose modalities. The results reveal an accuracy of less than 35%, showing the inherent level of difficulty of our dataset compared to other current datasets, in which accuracies are higher than 90%. Thus, our proposed dataset establishes a new level of difficulty in the gait recognition problem, much closer to real life.

Legged locomotion Extrapolation Shape Conferences Video sequences Clothing Time measurement

其他

英语

EI

20213410818324

Gait analysis ; Video recording

Bioengineering and Biology:461 ; Biomechanics, Bionics and Biomimetics:461.3 ; Television Systems and Equipment:716.4

2-s2.0-85113339885

Scopus

1.Shenzhen University,College of Computer Science and Software Engineering,China
2.University of Malaga,Department of Computer Architecture,Spain
3.University of Cordoba,Department of Computing and Numerical Analysis,Spain
4.Southern University of Science and Technology,Department of Computer Science and Engineering,China

Mu，Zihao,Castro，Francisco M.,Marin-Jimenez，Manuel J.,et al. ReSGait: The Real-Scene Gait Dataset[C],2021:1-8.