Performance Evaluation Method for Mobile Computer Vision Systems using Augmented Reality
Paper in proceedings, 2010

This paper describes a framework which uses augmented reality for evaluating the performance of mobile computer vision systems. Computer vision systems use primarily image data to interpret the surrounding world, e.g. to detect, classify and track objects. The performance of mobile computer vision systems acting in unknown environments is inherently difficult to evaluate since, often, obtaining ground truth data is problematic. The proposed novel framework exploits the possibility to add virtual agents into a real data sequence collected in an unknown environment, thus making it possible to efficiently create augmented data sequences, including ground truth, to be used for performance evaluation. Varying the content in the data sequence by adding different virtual agents is straightforward, making the proposed framework very flexible. The method has been implemented and tested on a pedestrian detection system used for automotive collision avoidance. Preliminary results show that the method has potential to replace and complement physical testing, for instance by creating collision scenarios, which are difficult to test in reality.

Active safety

Collision avoidance

I.4.8 [image processing and computer vision]: scene analysis

Computer vision

Augmented reality

Performance evaluation

I.3.8 [computer graphics]: application

Author

Jonas Nilsson

Chalmers, Signals and Systems, Systems and control, Mechatronics

Anders Ödblom

Volvo Cars

Jonas Fredriksson

Chalmers, Signals and Systems, Systems and control, Mechatronics

Adeel Zafar

Chalmers, Signals and Systems

Fahim Ahmed

Chalmers, Signals and Systems

IEEE Virtual Reality 2010, VR 2010; Waltham, MA; United States; 20 March 2010 through 24 March 2010

19-22

Areas of Advance

Transport

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1109/VR.2010.5444821

ISBN

978-142446258-2

More information

Latest update

11/26/2018