This contribution presents a robust pedestrian detection system at night that fuses a camera sensor and a scanning radar sensor on feature level. Each sensor defines an overdetermined set of features to be selected and parameterized using the supervised training algorithm AdaBoost. This technique assures an optimal selection and weighting of the features from both sensors depending on their discriminative power for the classification task. In the radar plane a new complex signal filter has been derived which describes a local similarity measure of velocity differences. In order to achieve realtime capability multiple classifiers are combined using a cascade.
Camera and imaging radar feature level sensorfusion for night vision pedestrian recognition
2009 IEEE Intelligent Vehicles Symposium ; 597-603
2009-06-01
1926645 byte
Conference paper
Electronic Resource
English
Camera and Imaging Radar Feature Level Sensorfusion for Night Vision Pedestrian Recognition
British Library Conference Proceedings | 2009
|