No
Yes
View More
View Less
Working...
Close
OK
Cancel
Confirm
System Message
Delete
Schedule
An unknown error has occurred and your request could not be completed. Please contact support.
Scheduled
Wait Listed
Personal Calendar
Speaking
Conference Event
Meeting
Interest
Schedule TBD
Conflict Found
This session is already scheduled at another time. Would you like to...
Loading...
Please enter a maximum of {0} characters.
Please enter a maximum of {0} words.
must be 50 characters or less.
must be 40 characters or less.
Session Summary
We were unable to load the map image.
This has not yet been assigned to a map.
Search Catalog
Reply
Replies ()
Search
New Post
Microblog
Microblog Thread
Post Reply
Post
Your session timed out.
This web page is not optimized for viewing on a mobile device. Visit this site in a desktop browser to access the full set of features.
2017 GTC San Jose

S7624 - Driver Monitoring: A Deep Learning Approach for Gaze Estimation

Session Speakers
Session Description

A driver monitoring camera will be a valuable component when it comes to autonomous driving for levels 3 & 4. The camera is able to distinguish the area of the drivers' attention. For this purpose the estimation of the gaze of the driver is needed. Additionally to signal "eyes on road," the user experience for HMI can be significantly improved. We'll present a deep learning approach that trains a neural network in an end-to-end manner. Small patches of the eye serve as input to a convolution neural network. The tradeoff between a deep and shallow net is an important aspect when it comes to a commercial product. The massive use of GPUs can help to find the best tradeoff between accuracy and number of needed FLOPS as well as the best suited DNN architecture.


Additional Session Information
All
Talk
AI for In-Vehicle Applications Computer Vision and Machine Vision Deep Learning and AI
Automotive
25 minutes
Session Schedule