Automatic Real-time Vehicle Classification by Image Colour Component Based Template Matching
This work addresses real-time vehicle classification for traffic monitoring systems, but it is incremental as it builds on existing template matching methods with a focus on color optimization.
The paper tackled the problem of real-time vehicle classification on low-cost hardware by introducing a fast template matching algorithm that uses optimal color band selection, achieving a processing rate of about 4 frames per second.
Selection of appropriate template matching algorithms to run effectively on real-time low-cost systems is always major issue. This is due to unpredictable changes in image scene which often necessitate more sophisticated real-time algorithms to retain image consistency. Inefficiency of low cost auxiliary hardware and time limitations are the major constraints in using these sorts of algorithms. The real-time system introduced here copes with these problems utilising a fast running template matching algorithm, which makes use of best colour band selection. The system uses fast running real-time algorithms to achieve template matching and vehicle classification at about 4 frames /sec. on low-cost hardware. The colour image sequences have been taken by a fixed CCTV camera overlooking a busy multi-lane road