DocumentCode
2996467
Title
A Multi-sensor Traffic Scene Dataset with Omnidirectional Video
Author
Koschorrek, Philipp ; Piccini, Tommaso ; Oberg, Per ; Felsberg, Michael ; Nielsen, Larry ; Mester, Rudolf
Author_Institution
Dept. of Electr. Eng., Linkoping Univ., Linkoping, Sweden
fYear
2013
fDate
23-28 June 2013
Firstpage
727
Lastpage
734
Abstract
The development of vehicles that perceive their environment, in particular those using computer vision, indispensably requires large databases of sensor recordings obtained from real cars driven in realistic traffic situations. These datasets should be time shaped for enabling synchronization of sensor data from different sources. Furthermore, full surround environment perception requires high frame rates of synchronized omnidirectional video data to prevent information loss at any speeds. This paper describes an experimental setup and software environment for recording such synchronized multi-sensor data streams and storing them in a new open source format. The dataset consists of sequences recorded in various environments from a car equipped with an omnidirectional multi-camera, height sensors, an IMU, a velocity sensor, and a GPS. The software environment for reading these data sets will be provided to the public, together with a collection of long multi-sensor and multi-camera data streams stored in the developed format.
Keywords
computer vision; public domain software; software engineering; traffic information systems; very large databases; video signal processing; GPS; IMU; computer vision; large databases; multisensor traffic scene dataset; omnidirectional video; open source format; realistic traffic situations; software environment; synchronized multisensor data streams; vehicle development; velocity sensor; Cameras; Computers; Global Positioning System; Robot sensing systems; Software; Synchronization; Visualization; automotive; benchmark; dataset; multi-sensor; omnidirectional; surround sensing; synchronized; traffic; vision;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision and Pattern Recognition Workshops (CVPRW), 2013 IEEE Conference on
Conference_Location
Portland, OR
Type
conf
DOI
10.1109/CVPRW.2013.110
Filename
6595954
Link To Document