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

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.

Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.



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