Recognition of Activities from Eye Gaze and Egocentric Video
This work addresses activity recognition for applications like assistive technology or human-computer interaction, but it is incremental as it builds on existing methods by integrating multiple feature types.
The paper tackled the problem of human activity recognition by combining eye gaze, ego-motion, and visual features from egocentric video, resulting in improved classification accuracy compared to using individual features alone.
This paper presents a framework for recognition of human activity from egocentric video and eye tracking data obtained from a head-mounted eye tracker. Three channels of information such as eye movement, ego-motion, and visual features are combined for the classification of activities. Image features were extracted using a pre-trained convolutional neural network. Eye and ego-motion are quantized, and the windowed histograms are used as the features. The combination of features obtains better accuracy for activity classification as compared to individual features.