In this paper a feature and model based approach to real-time vehicle tracking and classification is described. We proceed in two steps: 1) we establish correspondence between model and image features by an optimization algorithm; and 2) based on this correspondence, a matching vector is derived and used as input to either a Bayes classifier, a neural net or a combination of both. The current implementation updates the model parameters (position and scale) at a rate of 8-12 frames per second.
Real-time vehicle tracking and classification
1995-01-01
716995 byte
Conference paper
Electronic Resource
English
Real-Time Vehicle Tracking and Classification
British Library Conference Proceedings | 1995
|Real-Time Vehicle Tracking System
IEEE | 2024
|Real-time vehicle tracking on highway
IEEE | 2003
|Real-Time Vehicle Tracking on Highway
British Library Conference Proceedings | 2003
|