A hybrid framework for online recognition of activities of daily living in real-world settings

Farhood Negin 1 Serhan Cosar 1 Michal Koperski 1 Carlos Crispim-Junior 1, 2 Konstantinos Avgerinakis 3 Francois Bremond 1, 2 François Bremond 1 Sehan Cos¸arcos¸ar Carlos Crispim 1 Sehan Cosar
1 STARS - Spatio-Temporal Activity Recognition Systems
CRISAM - Inria Sophia Antipolis - Méditerranée
2 CobTek - Cognition Behaviour Technology
3 CERTH - Centre for Research & Technology (.)
Abstract : Many supervised approaches report state-of-the-art results for recognizing short-term actions in manually clipped videos by utilizing fine body motion information. The main downside of these approaches is that they are not applicable in real world settings. The challenge is different when it comes to unstructured scenes and long-term videos. Un-supervised approaches have been used to model the long-term activities but the main pitfall is their limitation to handle subtle differences between similar activities since they mostly use global motion information. In this paper, we present a hybrid approach for long-term human activity recognition with more precise recognition of activities compared to unsupervised approaches. It enables processing of long-term videos by automatically clipping and performing online recognition. The performance of our approach has been tested on two Activities of Daily Living (ADL) datasets. Experimental results are promising compared to existing approaches.
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Conference papers
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https://hal.inria.fr/hal-01384710
Contributor : Farhood Negin <>
Submitted on : Monday, November 21, 2016 - 11:46:08 AM
Last modification on : Thursday, January 31, 2019 - 11:30:03 AM

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Farhood Negin, Serhan Cosar, Michal Koperski, Carlos Crispim-Junior, Konstantinos Avgerinakis, et al.. A hybrid framework for online recognition of activities of daily living in real-world settings. 13th IEEE International Conference on Advanced Video and Signal Based Surveillance - AVSS 2016 , Aug 2016, Colorado springs, United States. ⟨10.1109/AVSS.2016.7738021⟩. ⟨hal-01384710⟩

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