Paper Info
Title
Fast And Robust Key Frame Extraction Method For Gesture Video Based On High-Level Feature Representation
Abstract
In gesture video, the inner-frame difference is too subtle to be projected via low-level features, and the gesture frames, expressing semantic information, are distributed only among the tiny part of the whole video frame. This paper introduces a fast and robust key frame extraction method for gesture video, founded upon high-level feature representation to extract the gesture key frame precisely without affecting the semantic information. Firstly, a gesture video segmentation model is designed by employing SSD, which classify gesture video into the semantic scene and the static scene. And then, the 2D-DWT-based perceptual hash algorithm is studied to extract candidate static key frames. Afterward, the multi-channel gradient magnitude frequency histogram (HGMF-MC) based on improved VGG16 is developed as a new image descriptor. Finally, a key frame extraction mechanism based on HGMF-MC is proposed to generate gesture video summary of two scenes, respectively. Experiments consistently show the superiority of the proposed method on Chinese sign language, Cambridge, ChaLearn and CVRR-Hands gesture datasets. The results demonstrate that the method proposed is effective, which improves the video compression ratio and outperforms the state-of-the-art methods.
Year
DOI
Venue
2021
10.1007/s11760-020-01783-4
SIGNAL IMAGE AND VIDEO PROCESSING
Keywords
DocType
Volume
Gesture video classification, Improved VGG16, The histogram of gradient magnitude frequency, 2D-DWT-based perceptual hash
Journal
15
Issue
ISSN
Citations 
3
1863-1703
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Huimin Yang100.34
Qiuhong Tian200.34
Qiaoli Zhuang300.34
Linye Li400.34
Qinglong Liang500.34