Subjective Annotations for Vision-Based Attention Level Estimation
This work addresses the lack of annotated datasets for attention level estimation, which is important for applications like human-robot interaction and smart home systems, but it is incremental as it builds on existing HCI research.
The authors tackled the problem of estimating human attention levels by introducing a new annotation framework for subjective attention and a method using geometric features from RGB and depth images, achieving an overall accuracy of 80.02%.
Attention level estimation systems have a high potential in many use cases, such as human-robot interaction, driver modeling and smart home systems, since being able to measure a person's attention level opens the possibility to natural interaction between humans and computers. The topic of estimating a human's visual focus of attention has been actively addressed recently in the field of HCI. However, most of these previous works do not consider attention as a subjective, cognitive attentive state. New research within the field also faces the problem of the lack of annotated datasets regarding attention level in a certain context. The novelty of our work is two-fold: First, we introduce a new annotation framework that tackles the subjective nature of attention level and use it to annotate more than 100,000 images with three attention levels and second, we introduce a novel method to estimate attention levels, relying purely on extracted geometric features from RGB and depth images, and evaluate it with a deep learning fusion framework. The system achieves an overall accuracy of 80.02%. Our framework and attention level annotations are made publicly available.