Multiple-Stage Classification of Human Poses while Watching Television

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Publication Details

Author listVisutarrom T., Mongkolnam P., Chan J.H.

PublisherHindawi

Publication year2015

Start page10

End page16

Number of pages7

ISBN9781479975525

ISSN0146-9428

eISSN1745-4557

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84937604275&doi=10.1109%2fISCBI.2014.10&partnerID=40&md5=2eefddd8df4dc5ddb1687226536d133c

LanguagesEnglish-Great Britain (EN-GB)


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Abstract

We compared the accuracy measure between a single-stage classifier model and a multiple-stage classifier model in postural classifications using Kinect. Postural training sets were collected from Kinect's skeletal data streams, based on some of the common human postures during television watching. Three types of training sets were used, including Kinect's raw skeletal training set, skeletons with attribute selection training set, and skeletal position transformation training set. We selected four learning models, namely, neural network, na๏ve Bayes, logistic regression, and decision tree, for learning our data sets and classifying a testing set to find the appropriate learning model. The best accuracy value of our experiment was 87.68 % by using skeletal position transformation training set with neural network. In the future, we will apply our technique and methodology to track elderly behaviors while they are watching television. ฉ 2014 IEEE.


Keywords

data transformationtelevision watching


Last updated on 2023-28-09 at 07:35