A hierarchical representation for human activity recognition with noisy labels

Ninghang Hu, Gwenn Englebienne, ZhongYu Lou, Ben Kröse

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Abstract

Human activity recognition is an essential task for robots to effectively and efficiently interact with the end users. Many machine learning approaches for activity recognition systems have been proposed recently. Most of these methods are built upon a strong assumption that the labels in the training data are noise-free, which is often not realistic. In this paper, we incorporate the uncertainty of labels into a max-margin learning algorithm, and the algorithm allows the labels to deviate over iterations in order to find a better solution. This is incorporated with a hierarchical approach where we jointly estimate activities at two different levels of granularity. The model is tested on two datasets, i.e., the CAD-120 dataset and the Accompany dataset, and the proposed model shows outperforming results over the state-of-the-art methods.
Original languageEnglish
Publication statusPublished - 2015
Externally publishedYes
Event2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015 - Hamburg, Germany
Duration: 28 Sept 20152 Oct 2015
https://iros2015.informatik.uni-hamburg.de/

Conference

Conference2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015
Abbreviated titleIROS
Country/TerritoryGermany
CityHamburg
Period28/09/152/10/15
Internet address

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