With the increasing availability of wearable devices, research on egocentric
activity recognition has received much attention recently. In this paper, we
build a Multimodal Egocentric Activity dataset which includes egocentric videos
and sensor data of 20 fine-grained and diverse activity categories. We present
a novel strategy to extract temporal trajectory-like features from sensor data.
We propose to apply the Fisher Kernel framework to fuse video and temporal
enhanced sensor features. Experiment results show that with careful design of
feature extraction and fusion algorithm, sensor data can enhance
information-rich video data. We make publicly available the Multimodal
Egocentric Activity dataset to facilitate future research.

本文介绍了一个基于 Wearable 设备的多模态自我中心活动识别数据集，其中提出了一种从传感器数据中提取时空轨迹特征的新颖策略，并应用 Fisher Kernel 框架融合视频和时空增强的传感器特征，实验结果表明，精心设计的特征提取和融合算法可以提高信息丰富的视频数据的质量。