Boosted multiple kernel learning for first-person activity recognition

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2017-09-02

Author

Özkan, Fatih
Arabacı, Mehmet Ali
Sürer, Elif
Temizel, Alptekin

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Activity recognition from first-person (ego-centric) videos has recently gained attention due to the increasing ubiquity of the wearable cameras. There has been a surge of efforts adapting existing feature descriptors and designing new descriptors for the first-person videos. An effective activity recognition system requires selection and use of complementary features and appropriate kernels for each feature. In this study, we propose a data-driven framework for first-person activity recognition which effectively selects and combines features and their respective kernels during the training. Our experimental results show that use of Multiple Kernel Learning (MKL) and Boosted MKL in first-person activity recognition problem exhibits improved results in comparison to the state-of-the-art. In addition, these techniques enable the expansion of the framework with new features in an efficient and convenient way.

Subject Keywords

Multiple kernel learning, Kernel boosting, Firstperson, Ego-centric videos, Activity recognition

URI

https://hdl.handle.net/11511/29977

DOI

https://doi.org/10.23919/eusipco.2017.8081368

Collections

Graduate School of Informatics, Conference / Seminar

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Citation Formats

F. Özkan, M. A. Arabacı, E. Sürer, and A. Temizel, “Boosted multiple kernel learning for first-person activity recognition,” 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/29977.