Bookmark and Share

"Approaches for Vision-based Driver Monitoring," a Presentation from PathPartner Technology

Register or sign in to access the Embedded Vision Academy's free technical training content.

The training materials provided by the Embedded Vision Academy are offered free of charge to everyone. All we ask in return is that you register, and tell us a little about yourself so that we can understand a bit about our audience. As detailed in our Privacy Policy, we will not share your registration information, nor contact you, except with your consent.

Registration is free and takes less than one minute. Click here to register, and get full access to the Embedded Vision Academy's unique technical training content.

If you've already registered, click here to sign in.

See a sample of this page's content below:

Jayachandra Dakala, Technical Architect at PathPartner Technology, presents the "Approaches for Vision-based Driver Monitoring" tutorial at the May 2017 Embedded Vision Summit.

Since many road accidents are caused by driver inattention, assessing driver attention is important to preventing accidents. Distraction caused by other activities and sleepiness due to fatigue are the main causes of driver inattention. Vision-based assessment of driver distraction and fatigue must estimate face pose, sleepiness, expression, etc. Estimating these aspects under real driving conditions, including day-to-night transition, drivers wearing sunglasses etc., is a challenging task.

A solution using deep learning to handle tasks from searching for a driver’s face in a given image to estimating attention would potentially be difficult to realize in an embedded system. In this talk, Dakala looks at the pros and cons of various machine learning approaches like multi-task deep networks, boosted cascades, etc. for this application, and then describes a hybrid approach that provides the required insights while being realizable in an embedded system.