EV-Gait: Event-Based Robust Gait Recognition Using Dynamic Vision Sensors

In this paper, we introduce a new type of sensing modality, the Dynamic Vision Sensors (Event Cameras), for the task of gait recognition. Compared with the traditional RGB sensors, the event cameras have many unique advantages such as ultra low resources consumption, high temporal resolution and muc...

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Bibliographic Details
Published in:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) pp. 6351 - 6360
Main Authors: Wang, Yanxiang, Du, Bowen, Shen, Yiran, Wu, Kai, Zhao, Guangrong, Sun, Jianguo, Wen, Hongkai
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2019
Subjects:
and Body Pose
Categorization
Face
Gesture
Others
Recognition: Detection
Retrieval
Vision Applications and Systems
ISSN:1063-6919
Online Access:Get full text
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Summary:In this paper, we introduce a new type of sensing modality, the Dynamic Vision Sensors (Event Cameras), for the task of gait recognition. Compared with the traditional RGB sensors, the event cameras have many unique advantages such as ultra low resources consumption, high temporal resolution and much larger dynamic range. However, those cameras only produce noisy and asynchronous events of intensity changes rather than frames, where conventional vision-based gait recognition algorithms can't be directly applied. To address this, we propose a new Event-based Gait Recognition (EV-Gait) approach, which exploits motion consistency to effectively remove noise, and uses a deep neural network to recognise gait from the event streams. To evaluate the performance of EV-Gait, we collect two event-based gait datasets, one from real-world experiments and the other by converting the publicly available RGB gait recognition benchmark CASIA-B. Extensive experiments show that EV-Gait can get nearly 96% recognition accuracy in the real-world settings, while on the CASIA-B benchmark it achieves comparable performance with state-of-the-art RGB-based gait recognition approaches.
ISSN:1063-6919
DOI:10.1109/CVPR.2019.00652