Title
A spatiotemporal attention-based ResC3D model for large-scale gesture recognition
Abstract
Abnormal gesture recognition has many applications in the fields of visual surveillance, crowd behavior analysis, and sensitive video content detection. However, the recognition of dynamic gestures with large-scale videos remains a challenging task due to the barriers of gesture-irrelevant factors like the variations in illumination, movement path, and background. In this paper, we propose a spatiotemporal attention-based ResC3D model for abnormal gesture recognition with large-scale videos. One key idea is to find a compact and effective representation of the gesture in both spatial and temporal contexts. To eliminate the influence of gesture-irrelevant factors, we first employ the enhancement techniques such as Retinex and hybrid median filer to improve the quality of RGB and depth inputs. Then, we design a spatiotemporal attention scheme to focus on the most valuable cues related to the moving parts for the gesture. Upon these representations, a ResC3D network, which leverages the advantages of both residual network and C3D model, is developed to extract features, together with a canonical correlation analysis-based fusion scheme for blending features from different modalities. The performance of our method is evaluated on the Chalearn IsoGD Dataset. Experiments demonstrate the effectiveness of each module of our method and show the ultimate accuracy reaches 68.14%, which outperforms other state-of-the-art methods, including our basic work in 2017 Chalearn Looking at People Workshop of ICCV.
Year
DOI
Venue
2019
10.1007/s00138-018-0996-x
Machine Vision and Applications
Keywords
Field
DocType
Gesture recognition, Spatiotemporal attention mechanism, ResC3D model
Modalities,Residual,Color constancy,Computer vision,Pattern recognition,Gesture,Computer science,Canonical correlation,Gesture recognition,RGB color model,Artificial intelligence,Crowd psychology
Journal
Volume
Issue
ISSN
30
5
1432-1769
Citations 
PageRank 
References 
2
0.36
40
Authors
5
Name
Order
Citations
PageRank
Yunan Li1172.33
Qiguang Miao235549.69
Xiangda Qi320.36
Zhenxin Ma4111.84
Wanli Ouyang52371105.17