Dimensionality reduction of Fisher vectors for human action recognition. Issue 5 (26th February 2016)

• Record Type: Journal Article
• Authors: Oruganti, Venkata Ramana Murthy; Goecke, Roland
• Is Part Of: IET computer vision. Volume 10:Issue 5(2016)
• Page Start: 392
• Page End: 397
• Publication Date: 2016-02-26
• Subjects: gesture recognition -- video signal processing; Fisher vectors -- human action recognition -- human behaviour automatic analysis -- file sharing sites -- YouTube -- action class -- action video -- feature projection -- high-dimensional feature vectors -- dimensionality reduction technique -- untrimmed videos -- bag-of-word framework -- large-class benchmark database -- HMDB51 dataset -- UCF101 dataset; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2015.0091
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : Automatic analysis of human behaviour in large collections of videos is rapidly gaining interest, even more so with the advent of file sharing sites such as YouTube. From one perspective, it can be observed that the size of feature vectors used for human action recognition from videos has been increasing enormously in the last five years, in the order of ∼100–500K. One possible reason might be the growing number of action classes/videos and hence the requirement of discriminating features (that usually end up to be higher‐dimensional for larger databases). In this study, the authors review and investigate feature projection as a means to reduce the dimensions of the high‐dimensional feature vectors and show their effectiveness in terms of performance. They hypothesise that dimensionality reduction techniques often unearth latent structures in the feature space and are effective in applications such as the fusion of high‐dimensional features of different types; and action recognition in untrimmed videos. They conduct all the authors' experiments using a Bag‐of‐Words framework for consistency and results are presented on large class benchmark databases such as the HMDB51 and UCF101 datasets.