Online recognition of daily activities by color-depth sensing and knowledge models

Abstract : Visual activity recognition plays a fundamental role in several research fields as a way to extract semantic meaning of images and videos. Prior work has mostly focused on classification tasks, where a label is given for a video clip. However, real life scenarios require a method to browse a continuous video flow, automatically identify relevant temporal segments and classify them accordingly to target activities. This paper proposes a knowledge-driven event recognition 5 framework to address this problem. The novelty of the method lies in the combination of a constraint-based ontology language for event modeling with robust algorithms to detect, track and re-identify people using color-depth sensing (Kinect sensor). This combination enables to model and recognize longer and more complex events and to incorporate domain knowledge and 3D information into the same models. Moreover, the ontology-driven approach enables human understanding of system decisions and facilitates knowledge transfer across different scenes. The proposed framework is evaluated with real-world recordings of seniors carrying out unscripted, daily activities at hospital observation rooms and nursing homes. Results demonstrated that the proposed framework outperforms state-of-the-art methods in a variety of activities and datasets, and it is robust to variable and low-frame rate recordings. Further work will investigate how to extend the proposed framework with uncertainty management techniques to handle strong occlusion and ambiguous semantics, and how to exploit it to further support medicine on the timely diagnosis of cognitive disorders, such as Alzheimer's disease.
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Sensors, MDPI, 2017, 17 (7), pp.1-15. 〈10.3390/s17071528〉
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Dernière modification le : jeudi 11 janvier 2018 - 16:57:48

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Carlos Fernando Crispim-Junior, Alvaro Gómez Uría, Carola Strumia, Michal Koperski, Alexandra Konig, et al.. Online recognition of daily activities by color-depth sensing and knowledge models. Sensors, MDPI, 2017, 17 (7), pp.1-15. 〈10.3390/s17071528〉. 〈hal-01658438〉

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