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Practical action recognition with manifold regularized sparse representation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

With the explosion of long term health conditions, monitoring human daily activities in home environment is one of the important issues in healthcare. Human action recognition in videos is one of the main topics in this context. Conventional representations are not very effective for encoding dense features extracted from videos. In this work, we propose a novel manifold regularized sparse representation (MRSR) method to encode dense features for human action recognition in assisted living. The new method can effectively incorporate a manifold regularization term to explore the geometric structure of the improved dense trajectories, which are very effective to learn action representations. By introducing a locality constraint, our method can ensure each interest point is represented by its local closest words. Moreover, our method has an analytical solution and low computational complexity. Experimental results on different realistic databases show the effectiveness of the proposed algorithm for practical action recognition in assisted living.
Original languageEnglish
Title of host publicationThe British Machine Vision Conference
PublisherBritish Machine Vision Association
Number of pages11
StatePublished - 6 Sep 2018
Event29th British Machine Vision Conference - Northumbria, Newcastle, United Kingdom
Duration: 3 Sep 20186 Sep 2018
http://bmvc2018.org/
http://bmvc2018.org/

Conference

Conference29th British Machine Vision Conference
Abbreviated titleBMVC 2018
CountryUnited Kingdom
CityNewcastle
Period3/09/186/09/18
Internet address

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