Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/22877
Author(s): | Jardim, D. Nunes, L. Dias, M. |
Editor: | Verikas, A., Radeva, P., Nikolaev, D. P., Zhang, W. and Zhou, J. |
Date: | 1-Jan-2017 |
Title: | Predicting human activities in sequences of actions in RGB-D videos |
Volume: | 10341 |
Event title: | 9th International Conference on Machine Vision, ICMV 2016 |
ISSN: | 0277-786X |
ISBN: | 978-1-5106-1132-0 |
DOI (Digital Object Identifier): | 10.1117/12.2268524 |
Keywords: | Human motion analysis Recognition Segmentation Clustering Labeling Kinect Prediction Anticipation |
Abstract: | In our daily activities we perform prediction or anticipation when interacting with other humans or with objects. Prediction of human activity made by computers has several potential applications: surveillance systems, human computer interfaces, sports video analysis, human-robot-collaboration, games and health-care. We propose a system capable of recognizing and predicting human actions using supervised classifiers trained with automatically labeled data evaluated in our human activity RGB-D dataset (recorded with a Kinect sensor) and using only the position of the main skeleton joints to extract features. Using conditional random fields (CRFs) to model the sequential nature of actions in a sequence has been used before, but where other approaches try to predict an outcome or anticipate ahead in time (seconds), we try to predict what will be the next action of a subject. Our results show an activity prediction accuracy of 89.9% using an automatically labeled dataset. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | ISTAR-CRI - Comunicações a conferências internacionais |
Files in This Item:
File | Description | Size | Format | |
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conferenceobject_42663.pdf | Versão Aceite | 418,62 kB | Adobe PDF | View/Open |
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