Most of the exist action recognition methods mainly utilize spatio-temporal descriptors of single interest point while ignoring their potential integral information, such as spatial distribution information. By combining local spatio-temporal feature and global positional distribution information (PDI) of interest points, a novel motion descriptor is proposed in this paper. The proposed method detects interest points by using an improved interest point detection method. Then, 3-dimensional scale-invariant feature transform (3D SIFT) descriptors are extracted for every interest point. In order to obtain a compact description and efficient computation, the principal component analysis (PCA) method is utilized twice on the 3D SIFT descriptors of single frame and multiple frames. Simultaneously, the PDI of the interest points are computed and combined with the above features. The combined features are quantified and selected and finally tested by using the support vector machine (SVM) recognition algorithm on the public KTH dataset. The testing results have showed that the recognition rate has been significantly improved and the proposed features can more accurately describe human motion with high adaptability to scenarios.
Study of human action recognition based on improved spatio-temporal features
2015-10-01
Ji , X , Wu , Q , Ju , Z & Wang , Y 2015 , ' Study of human action recognition based on improved spatio-temporal features ' International Journal of Automation and Computing , vol 11 , no. 5 , pp. 500-509 . DOI:10.1007/s11633-014-0831-4
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC: | 629 |
Study of human action recognition based on improved spatio-temporal features
British Library Online Contents | 2014
|Action recognition via spatio-temporal local features: A comprehensive study
British Library Online Contents | 2016
|Action recognition using global spatio-temporal features derived from sparse representations
British Library Online Contents | 2014
|Spatio-temporal action localization for human action recognition in large dataset [9407-22]
British Library Conference Proceedings | 2015
British Library Online Contents | 2014
|