A robust framework to classify vehicles in nighttime traffic using vehicle eigenspaces and support vector machine is presented. In this paper, a systematic approach has been proposed and implemented to classify vehicles from roadside camera video sequences. Collections of vehicle images are analyzed to obtain their representative eigenspaces. The model Support Vector Machine (SVM) built from those vehicle spaces will then become a reliable classifier for any unknown vehicle images. This approach has been implemented and proven to be robust in both speed and accuracy for vehicle classification at night.
Vehicle Classification at Nighttime Using Eigenspaces and Support Vector Machine
2008 Congress on Image and Signal Processing ; 2 ; 422-426
01.05.2008
454688 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Illumination Insensitive Eigenspaces
British Library Conference Proceedings | 2001
|Illumination insensitive eigenspaces
IEEE | 2001
|Illumination insensitive recognition using eigenspaces
British Library Online Contents | 2004
|Pose Estimation Using 3D View-Based Eigenspaces
British Library Conference Proceedings | 2003
|Vehicle Classification in Nighttime Using Headlights Trajectories Matching
Springer Verlag | 2018
|