Title
Digging Deeper Into Egocentric Gaze Prediction
Abstract
This paper digs deeper into factors that influence egocentric gaze. Instead of training deep models for this purpose in a blind manner, we propose to inspect factors that contribute to gaze guidance during daily tasks. Bottom-up saliency and optical flow are assessed versus strong spatial prior baselines. Task-specific cues such as vanishing point, manipulation point, and hand regions are analyzed as representatives of top-down information. We also look into the contribution of these factors by investigating a simple recurrent neural model for ego-centric gaze prediction. First, deep features are extracted for all input video frames. Then, a gated recurrent unit is employed to integrate information over time and to predict the next fixation. We propose an integrated model that combines the recurrent model with several top-down and bottom-up cues. Extensive experiments over multiple datasets reveal that (1) spatial biases are strong in egocentric videos, (2) bottom-up attention models perform poorly in predicting gaze and underperform spatial biases, (3) deep features perform better compared to traditional features, (4) as opposed to hand regions, the manipulation point is a strong influential cue for gaze prediction, (5) combining the proposed recurrent model with bottom-up cues, vanishing points and, in particular, manipulation point results in the best gaze prediction accuracy over egocentric videos, (6) the knowledge transfer works best for cases where the tasks or sequences are similar, and (7) task and activity recognition can benefit from gaze prediction. Our findings suggest that (1) there should be more emphasis on hand-object interaction and (2) the egocentric vision community should consider larger datasets including diverse stimuli and more subjects.
Year
DOI
Venue
2019
10.1109/WACV.2019.00035
2019 IEEE Winter Conference on Applications of Computer Vision (WACV)
Keywords
DocType
Volume
Task analysis,Computational modeling,Predictive models,Feature extraction,Visualization,Cameras,Computer vision
Journal
abs/1904.06090
ISSN
ISBN
Citations 
2472-6737
978-1-7281-1975-5
1
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Hamed Rezazadegan Tavakoli114813.23
Esa Rahtu283252.76
Juho Kannala386760.91
Ali Borji4198578.50