Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2014

Abstract

Recently, approaches utilizing spatial-temporal features have achieved great success in human action classification. However, they typically rely on bag-of-words (BoWs) model, and ignore the spatial and temporal structure information of visual words, bringing ambiguities among similar actions. In this paper, we present a novel approach called sequential BoWs for efficient human action classification. It captures temporal sequential structure by segmenting the entire action into sub-actions. Each sub-action has a tiny movement within a narrow range of action. Then the sequential BoWs are created, in which each sub-action is assigned with a certain weight and salience to highlight the distinguishing sections. It is noted that the weight and salience are figured out in advance according to the sub-action’s discrimination evaluated by training data. Finally, those sub-actions are used for classification respectively, and voting for united result. Experiments are conducted on UT-interaction dataset and Rochester dataset. The results show its higher robustness and accuracy over most state-of-the-art classification approaches.

Keywords

Human action recognition, sequential model, bag-of-words

Discipline

Computer Engineering | Software Engineering

Research Areas

Data Science and Engineering

Publication

Proceddings of the 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014), Bali, December 5-10

First Page

2280

Last Page

2285

Identifier

10.1109/ROBIO.2014.7090677

City or Country

Bali

Additional URL

https://doi.org/10.1109/ROBIO.2014.7090677

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