Vehicle Classification at Nighttime Using Eigenspaces and Support Vector Machine

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.