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Proceedings Paper

Predicting human activities in sequences of actions in RGB-D videos
Author(s): David Jardim; Luís Nunes; Miguel Dias
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Paper 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.

Paper Details

Date Published: 17 March 2017
PDF: 5 pages
Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103410C (17 March 2017); doi: 10.1117/12.2268524
Show Author Affiliations
David Jardim, Microsoft Language Development Ctr. (Portugal)
Intituto Univ. de Lisboa (Portugal)
Instituto de Telecomunicações and ISTAR-IUL (Portugal)
Luís Nunes, Instituto Univ. de Lisboa (Portugal)
Instituto de Telecomunicações (Portugal)
ISTAR-IUL (Portugal)
Miguel Dias, Instituto Univ. de Lisboa (Portugal)
Instituto de Telecomunicações (Portugal)
ISTAR-IUL (Portugal)


Published in SPIE Proceedings Vol. 10341:
Ninth International Conference on Machine Vision (ICMV 2016)
Antanas Verikas; Petia Radeva; Dmitry P. Nikolaev; Wei Zhang; Jianhong Zhou, Editor(s)

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