Boosted Multiple Kernel Learning for First-Person Activity Recognition

TL;DR

This paper introduces a data-driven approach using Boosted Multiple Kernel Learning to improve first-person activity recognition by effectively selecting and combining features and kernels, outperforming existing methods.

Contribution

It presents a novel Boosted MKL framework that enhances feature selection and kernel combination for first-person activity recognition, with improved accuracy and scalability.

Findings

Boosted MKL outperforms state-of-the-art methods.

Framework efficiently incorporates new features.

Improved activity recognition accuracy.

Abstract

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.