Paper Info
Title
ChaLearn Looking at People: IsoGD and ConGD Large-Scale RGB-D Gesture Recognition
Abstract
The ChaLearn large-scale gesture recognition challenge has run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV) 2017, attracting more than 200 teams around the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. It describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. In this article, we discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition and provide a detailed analysis of the current methods for large-scale isolated and continuous gesture recognition. In addition to the recognition rate and mean Jaccard index (MJI) as evaluation metrics used in previous challenges, we introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) method, determining video division points based on skeleton points. Experiments show that the proposed Bi-LSTM outperforms state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR.
Year
DOI
Venue
2022
10.1109/TCYB.2020.3012092
IEEE Transactions on Cybernetics
Keywords
DocType
Volume
Algorithms,Gestures,Humans,Pattern Recognition, Automated
Journal
52
Issue
ISSN
Citations 
5
2168-2267
1
PageRank 
References 
Authors
0.35
60
10
Name
Order
Citations
PageRank
Jun Wan125522.37
chi lin2504.81
Longyin Wen364733.89
yunan li4172.68
Qiguang Miao535549.69
Sergio Escalera61415113.31
Gholamreza Anbarjafari734736.51
Isabelle Guyon8110331544.34
Guodong Guo92548144.00
Stan Z. Li108951535.26