Paper Info
Title
Human Action Recognition Based On Oriented Motion Salient Regions
Abstract
Motion is the most informative cue for human action recognition. Regions with high motion saliency indicate where actions occur and contain visual information that is most relevant to actions. In this paper, we propose a novel approach for human action recognition based on oriented motion salient regions (OMSRs). Firstly, we apply a bank of 3D Gabor filters and an opponent inhibition operator to detect OMSRs of videos, each of which corresponds to a specific motion direction. Then, a new low-level feature, named as oriented motion salient descriptor (OMSD), is proposed to describe the obtained OMSRs through the statistics of the texture in the regions. Next, we utilize the obtained OMSDs to explore the oriented characteristics of action classes and generate a set of class-specific oriented attributes (CSOAs) for each class. These CSOAs provide a compact and discriminative middle-level representation for human actions. Finally, an SVM classifier is utilized for human action classification and a new compatibility function is devised for measuring how well a given action matches to the CSOAs of a certain class. We test the proposed approach on four public datasets and the experimental results validate the effectiveness of our approach.
Year
DOI
Venue
2014
10.1007/978-3-319-16628-5_9
COMPUTER VISION - ACCV 2014 WORKSHOPS, PT I
Field
DocType
Volume
Computer vision,Pattern recognition,High-motion,Computer science,Salience (neuroscience),Action recognition,Motion direction,Gabor filter,Operator (computer programming),Artificial intelligence,Discriminative model,Salient
Conference
9008
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
22
5
Name
Order
Citations
PageRank
Baoxin Wu1322.08
Shuang Yang2172.06
Chunfeng Yuan341830.84
Weiming Hu45300261.38
Fangshi Wang5214.74