DocumentCode
159732
Title
Computer vision and laser scanner road environment perception
Author
Garcia, Francisco ; Ponz, A. ; Martin, Daniel ; de la Escalera, A. ; Armingol, J.M.
Author_Institution
Univ. Carlos III de Madrid, Leganes, Spain
fYear
2014
fDate
12-15 May 2014
Firstpage
63
Lastpage
66
Abstract
Data fusion procedure is presented to enhance classical Advanced Driver Assistance Systems (ADAS). The novel vehicle safety approach, combines two classical sensors: computer vision and laser scanner. Laser scanner algorithm performs detection of vehicles and pedestrians based on pattern matching algorithms. Computer vision approach is based on Haar-Like features for vehicles and Histogram of Oriented Gradients (HOG) features for pedestrians. The high level fusion procedure uses Kalman Filter and Joint Probabilistic Data Association (JPDA) algorithm to provide high level detection. Results proved that by means of data fusion, the performance of the system is enhanced.
Keywords
Haar transforms; Kalman filters; computer vision; driver information systems; image fusion; image matching; image sensors; object detection; optical scanners; pedestrians; probability; road safety; ADAS; Haar-like features; JPDA algorithm; Kalman filter; classical advanced driver assistance systems; classical sensors; computer vision; high level data fusion procedure; high level fusion procedure; histogram of oriented gradient features; joint probabilistic data association algorithm; laser scanner algorithm; pattern matching algorithms; pedestrian detection; road environment perception; vehicle detection; vehicle safety approach; Classification algorithms; Clutter; Data integration; Equations; Filtering algorithms; Mathematical model; Rotation measurement; ADAS; Computer Vision; Data Fusion; Laser Scanner; Pedestrian Detection;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Signals and Image Processing (IWSSIP), 2014 International Conference on
Conference_Location
Dubrovnik
ISSN
2157-8672
Type
conf
Filename
6837631
Link To Document