Authors: S. Gokul; S. Suresh Kumar; S. Giriprasad
Addresses: Department of Electrical and Electronics Engineering, Coimbatore Institute of Engineering and Technology, Coimbatore, Tamil Nadu, 641109, India ' Department of Electronics and Communication Engineering, Dr. N.G.P. Institute of Technology, Coimbatore, Tamil Nadu, 641048, India ' Department of Electronics and Communication Engineering, Coimbatore Institute of Engineering and Technology, Coimbatore, Tamil Nadu, 641109, India
Abstract: Traffic panels provide vital information on roads with the aid of iconic symbols and text strings. Recognising these signs accurately at the right time is crucial for car drivers to ensure safe journey. The automatic visual recognition and classification of the information contained in the panel could be very useful for driver assistance application. In this paper, a method is proposed to identify and to recognise the information contained in the traffic panels, as an application utilising colour space conversion for image acquisition then the colour decomposition and shape model generation using active appearance model (AAM). The traffic panel is detected using character descriptor-based adaptive fuzzy clustering (AFC). Multiframe detection strategy is a simple way of using precision and recall. Finally the prediction accuracy is computed by using massive training artificial neural network (MTANN). The efficiency of the system is evaluated with the help of the MATLAB-based experimental results which are compared with the bag of visual words for text geolocation and recognising symbols and texts (BTG & ST) method and shape and colour (SC)-based traffic sign detection methods in terms of the sensitivity, specificity and recognition rate.
Keywords: traffic panel; iconic symbols; AAM; active appearance model AFC; adaptive fuzzy clustering; MTANN; massive training artificial neural network.
International Journal of Heavy Vehicle Systems, 2018 Vol.25 No.3/4, pp.322 - 343
Received: 20 Apr 2017
Accepted: 22 Jun 2017
Published online: 24 Sep 2018 *