# Egocentric Activity Recognition with Multimodal Fisher Vector

arXiv: https://arxiv.org/abs/1601.06603 · HTML: https://mlsanity.com/paper/1601.06603/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2016.

## Built on by (most cited first, 5 of 5)

- [WEAR: An Outdoor Sports Dataset for Wearable and Egocentric Activity Recognition](https://mlsanity.com/paper/2304.05088/) (2023)
- [FT-HID: A Large Scale RGB-D Dataset for First and Third Person Human Interaction Analysis](https://mlsanity.com/paper/2209.10155/) (2022)
- [MMTSA: Multimodal Temporal Segment Attention Network for Efficient Human Activity Recognition](https://mlsanity.com/paper/2210.09222/) (2022)
- [A Survey on Human Action Recognition](https://mlsanity.com/paper/2301.06082/) (2022)
- [Joint Architecture-Token-Bitwidth Multi-Axis Optimization of Vision Transformers for Semiconductor IC Packaging](https://mlsanity.com/paper/2605.01742/) (2026)

## Abstract (arXiv)

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.