End-to-End Multitask Learning for Driver Gaze and Head Pose Estimation

End-to-End Video Captioning with Multitask Reinforcement Learning

Although end-to-end (E2E) learning has led to promising performance on a variety of tasks, it is often impeded by hardware constraints (e.g., GPU memories) and is prone to overfitting. When it comes to video captioning, one of the most challenging benchmark tasks in computer vision and machine learning, those limitations of E2E learning are especially amplified by the fact that both the input v...

End-to-End Deep Learning for Driver Distraction Recognition

In this paper, an end-to-end deep learning solution for driver distraction recognition is presented. In the proposed framework, the features from pre-trained convolutional neural networks VGG-19 are extracted. Despite the variation in illumination conditions, camera position, driver’s ethnicity, and genders in our dataset, our best fine-tuned model, VGG-19 has achieved the highest test accuracy...

Learning gaze biases with head motion for head pose-free gaze estimation

A Head Pose-free Approach for Appearance-based Gaze Estimation

(a) (b) (c) Figure 1: 2-D illustration of relationship between gaze direction and head pose. (a) Under a fixed head pose (r0, t0), gaze direction α can be estimated from appearance. (b) To obtain α under another head pose (r̂, t̂), the estimated α ′ should be corrected because of captured eye appearance distortion. (c) Under head pose (r̂, t̂), gaze direction under WCS should be further compensated...

'Owl' and 'Lizard': patterns of head pose and eye pose in driver gaze classification

Accurate, robust, inexpensive gaze tracking in the car can help keep a driver safe by facilitating the more effective study of how to improve (1) vehicle interfaces and (2) the design of future Advanced Driver Assistance Systems. In this paper, we estimate head pose and eye pose from monocular video using methods developed extensively in prior work and ask two new interesting questions. First, ...