# Egocentric Hand Track and Object-based Human Action Recognition

## TL;DR

This paper presents a method for egocentric human action recognition that relies solely on hand and object region detection and tracking, without using visual features, demonstrating comparable performance to feature-based methods.

## Contribution

The authors introduce a pipeline for detecting and tracking hands and relevant objects in egocentric videos, emphasizing the importance of scene context for action recognition.

## Key findings

- Hand and object detection alone can classify actions effectively.
- Region-of-interest-based video descriptions are as informative as visual features.
- The method performs well on unseen egocentric videos from the Epic-Kitchens dataset.

## Abstract

Egocentric vision is an emerging field of computer vision that is characterized by the acquisition of images and video from the first person perspective. In this paper we address the challenge of egocentric human action recognition by utilizing the presence and position of detected regions of interest in the scene explicitly, without further use of visual features. Initially, we recognize that human hands are essential in the execution of actions and focus on obtaining their movements as the principal cues that define actions. We employ object detection and region tracking techniques to locate hands and capture their movements. Prior knowledge about egocentric views facilitates hand identification between left and right. With regard to detection and tracking, we contribute a pipeline that successfully operates on unseen egocentric videos to find the camera wearer's hands and associate them through time. Moreover, we emphasize on the value of scene information for action recognition. We acknowledge that the presence of objects is significant for the execution of actions by humans and in general for the description of a scene. To acquire this information, we utilize object detection for specific classes that are relevant to the actions we want to recognize. Our experiments are targeted on videos of kitchen activities from the Epic-Kitchens dataset. We model action recognition as a sequence learning problem of the detected spatial positions in the frames. Our results show that explicit hand and object detections with no other visual information can be relied upon to classify hand-related human actions. Testing against methods fully dependent on visual features, signals that for actions where hand motions are conceptually important, a region-of-interest-based description of a video contains equally expressive information with comparable classification performance.