Advanced Driving Assisitance System
Developing an automotive-based algorithm for detecting pedestrians and vehicles
PI: Mohan Trivedi and Nuno Vasconcelos | International Partner: NEXTCHIP
Development of automotive-based object recognition algorithm for advanced driving assistance system (ADAS) to:
- Develop technology that provides driver a seamlessly composed, real-time top-view image using 4 HD cameras with fish-eye lens installed around a car.
- Image recognition technology: Develop real-time object recognition technology for the detection of pedestrians, traffic lanes, obstacles, parking space and distance between cars, in an urban driving condition.
- Lead Korea Organization: NextChip
- Lead US organization: Calit2 UCSD (Mohan Trivedi mtrivedi@soe.ucsd.edu, Nuno Vascocelos nuno@ece.ucsd.edu)
- Project Members:
- Ravi Kumar Satzoda Mohammad Saberian mj.saberian@gmail.com, Eshed Ohn-Bar eshedob@gmail.com
People:
Mohan Trivedi, Professor, Electrical and Computer Engineering, Jacobs School of Engineering; Director, Computer Vision and Robotics Research Laboratory; Ph.D. 1979 from Utah State.
Computer vision, robotics and sensors for cutting-edge applications, including "smart cars" (telematics), intelligent transportation systems, “smart rooms" (intelligent environments), biometrics (facial recognition) and sensor-based intelligent systems.
Website: http://cvrr.ucsd.edu/
Nuno Vasconcelos, Professor, Electrical and Computer Engineering, Jacobs School of Engineering; Head, Statistical Visual Computing Lab; Ph.D. 2000 from MIT.
Statistical signal processing, computer vision, machine learning, and multimedia. Topics include computational modeling of biological vision systems, object recognition and tracking, action recognition, machine learning algorithms, and multimedia search, classification and retrieval.
Website: www.svcl.ucsd.edu/~nuno/
The team stands outside the Laboratory for Intelligent & Safe Automobiles (LISA)
Two students go over specs with Vasconcelos