Fast realistic multi-action recognition using mined dense spatio-temporal features

Gilbert, Andrew, Illingworth, John and Bowden, Richard (2009) Fast realistic multi-action recognition using mined dense spatio-temporal features In: 12th IEEE International Conference on Computer Vision, 29 September - 2 October 2009, Kyoto, Japan.

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Abstract

Within the field of action recognition, features and descriptors are often engineered to be sparse and invariant to transformation. While sparsity makes the problem tractable, it is not necessarily optimal in terms of class separability and classification. This paper proposes a novel approach that uses very dense corner features that are spatially and temporally grouped in a hierarchical process to produce an overcomplete compound feature set. Frequently reoccurring patterns of features are then found through data mining, designed for use with large data sets. The novel use of the hierarchical classifier allows real time operation while the approach is demonstrated to handle camera motion, scale, human appearance variations, occlusions and background clutter. The performance of classification, outperforms other state-of-the-art action recognition algorithms on the three datasets; KTH, multi-KTH, and Hollywood. Multiple action localisation is performed, though no groundtruth localisation data is required, using only weak supervision of class labels for each training sequence. The Hollywood dataset contain complex realistic actions from movies, the approach outperforms the published accuracy on this dataset and also achieves real time performance. ©2009 IEEE.

Item Type:	Conference or Workshop Item (Conference Poster)
Divisions :	Surrey research (other units)
Authors :	Name Email ORCID Gilbert, Andrew A.Gilbert@surrey.ac.uk Illingworth, John J.Illingworth@surrey.ac.uk Bowden, Richard R.Bowden@surrey.ac.uk
Date :	2009
DOI :	10.1109/ICCV.2009.5459335
Copyright Disclaimer :	Copyright IEEE 2009
Depositing User :	Symplectic Elements
Date Deposited :	28 Mar 2017 14:42
Last Modified :	23 Jan 2020 12:46
URI:	http://epubs.surrey.ac.uk/id/eprint/531472

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