Back to Search Start Over

SPATIO-TEMPORAL CO-OCCURRENCE CHARACTERIZATIONS FOR HUMAN ACTION CLASSIFICATION.

Authors :
Md Sabri, Aznul Qalid
Boonaert, Jacques
Faizal Abdullah, Erma Rahayu Mohd
Mansoor, Ali Mohammed
Source :
Malaysian Journal of Computer Science; 2017, Vol. 30 Issue 3, p154-173, 20p
Publication Year :
2017

Abstract

The human action classification task is a widely researched topic and is still an open problem. Many state-ofthe- arts approaches involve the usage of bag-of-video-words with spatio-temporal local features to construct characterizations for human actions. In order to improve beyond this standard approach, we investigate the usage of co-occurrences between local features. We propose the usage of co-occurrences information to characterize human actions. A trade-off factor is used to define an optimal trade-off between vocabulary size and classification rate. Next, a spatio-temporal co-occurrence technique is applied to extract co-occurrence information between labeled local features. Novel characterizations for human actions are then constructed. These include a vector quantized correlogram-elements vector, a highly discriminative PCA (Principal Components Analysis) co-occurrence vector and a Haralick texture vector. Multi-channel kernel SVM (support vector machine) is utilized for classification. For evaluation, the well known KTH as well as the UCF-Sports action datasets are used. We obtained state-of-the-arts classification performance. We also demonstrated that we are able to fully utilize co-occurrence information, and improve the standard bag-of-video-words approach. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01279084
Volume :
30
Issue :
3
Database :
Supplemental Index
Journal :
Malaysian Journal of Computer Science
Publication Type :
Academic Journal
Accession number :
126398001
Full Text :
https://doi.org/10.22452/mjcs.vol30no3.1