Boosted Multiple Kernel Learning for First-Person Activity Recognition
This work addresses activity recognition from ego-centric videos, which is important for applications like wearable cameras, but it is incremental as it builds on existing MKL techniques.
The authors tackled first-person activity recognition by proposing a data-driven framework that selects and combines features and kernels using Multiple Kernel Learning (MKL) and Boosted MKL, resulting in improved performance compared to state-of-the-art methods.
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.