Emergency braking systems are among the most effective assistance systems in the car. In Germany alone, up to 72 percent of all rear-end collisions resulting in personal injury could be avoided if all vehicles were equipped with them. Now Bosch has developed a stereo video camera with which an emergency braking system can function based Read More →

TRW Automotive announced at the Auto Shanghai motor show that its Global Electronics business unit is launching its next generation video camera sensor – the S-Cam 3 – in China for the first time in early 2016. The technology will launch with a major North American vehicle manufacturer on several vehicles in the Chinese market, Read More →

Original post is by Andrew Davison on Google+.

The source code has just been released for ORB-SLAM by Raul Mur-Artal, +J.M. Martínez Montiel and +Mingo Tardós from the University of Zaragoza. This is a real-time monocular SLAM system which works in many different scenarios (indoor, outdoor, small or large scale) and these are probably the most accurate large scale single camera only (no IMU) SLAM results I’ve ever seen. It’s based on ORB features and a scale drift aware BA back-end. Great competition for LSD-SLAM (and it’s very interesting to look at the pros and cons of  these two approaches). Raul, congrats on getting the code out!
Code: https://github.com/raulmur/ORB_SLAM
Project Webpage: http://webdiis.unizar.es/~raulmur/orbslam/

When a very young child looks at a picture, she can identify simple elements: “cat,” “book,” “chair.” Now, computers are getting smart enough to do that too. What’s next? In a thrilling talk, computer vision expert Fei-Fei Li describes the state of the art — including the database of 15 million photos her team built to “teach” a computer to understand pictures — and the key insights yet to come.

Most of you know the speaker or are familiar with the problem (and of course the solution!). However, it still worth hearing it from one of the most pioneering women in this field.

Soure from ComputerVisionOnline.