A human activity recognition framework using max-min features and key poses with differential evolution random forests classifier


This paper presents a novel framework for human daily activity recognition that is intended to rely on few training examples evidencing fast training times, making it suitable for real-time applications. The proposed framework starts with a feature extraction stage, where the division of each activity into actions of variable-size, based on key poses, is performed. Each action window is delimited by two consecutive and automatically identified key poses, where static (i.e. geometrical) and max-min dynamic (i.e. temporal) features are extracted. These features are first used to train a random forest (RF) classifier which was tested using the CAD-60 dataset, obtaining relevant overall average results. Then in a second stage, an extension of the RF is proposed, where the differential evolution meta-heuristic algorithm is used, as splitting node methodology. The main advantage of its inclusion is the fact that the differential evolution random forest has no thresholds to tune, but rather a few adjustable parameters with well-defined behavior.

Publication DOI: https://doi.org/10.1016/j.patrec.2017.05.004
Divisions: College of Engineering & Physical Sciences
Additional Information: © 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords: human activity recognition,max-min features,key pose,random forests,differential evolution,Software,Signal Processing,Computer Vision and Pattern Recognition,Artificial Intelligence
Publication ISSN: 1872-7344
Last Modified: 14 May 2024 07:10
Date Deposited: 10 May 2017 14:25
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2017-11-01
Published Online Date: 2017-05-02
Accepted Date: 2017-05-01
Authors: Nunes, Urbano Miguel
Faria, Diego R. (ORCID Profile 0000-0002-2771-1713)
Peixoto, Paulo

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