Title :
Action recognition using instance-specific and class-consistent cues
Author :
Chin-An Lin ; Yen-Yu Lin ; Liao, Hong-Yuan Mark ; Shyh-Kang Jeng
Author_Institution :
Res. Center for Inf. Technol. Innovation, Acad. Sinica, Taipei, Taiwan
fDate :
Sept. 30 2012-Oct. 3 2012
Abstract :
We aim to resolve the difficulties of action recognition arising from the large intra-class variations. These unfavorable variations make it infeasible to represent one action instance by other ones of the same action. We hence propose to extract both instance-specific and class-consistent features to facilitate action recognition. Specifically, the instance-specific features explore the self-similarities among frames of each video instance, while class-consistent features summarize within-class similarities. We introduce a generative formulation to combine the two diverse types of features. The experimental results demonstrate the effectiveness of our approach.
Keywords :
feature extraction; gesture recognition; video signal processing; action recognition; class-consistent cues; class-consistent features; instance-specific cues; instance-specific features; large intra-class variations; video instance; Computer vision; Feature extraction; Hidden Markov models; Humans; Pattern recognition; Support vector machines; Trajectory; Action recognition; video understanding;
Conference_Titel :
Image Processing (ICIP), 2012 19th IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4673-2534-9
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2012.6467124