Improved efficiency of road sign detection and recognition by employing Kalman filter : 6th International Conference, BICS 2013, Beijing, China, June 9-11, 2013. Proceedings

Zakir, Usman and Hussain, Amir and Ali, Liaqat and Luo, Bin; Liu, Derong and Alippi, Cesare and Zhao, Dongbin and Hussain, Amir, eds. (2013) Improved efficiency of road sign detection and recognition by employing Kalman filter : 6th International Conference, BICS 2013, Beijing, China, June 9-11, 2013. Proceedings. In: Advances in Brain Inspired Cognitive Systems. Lecture Notes in Computer Science . Springer Berlin Heidelberg, CHN, pp. 216-224. ISBN 9783642387852

Full text not available in this repository.Request a copy from the Strathclyde author

Abstract

This paper describes an efficient approach towards road sign detection, and recognition. The proposed system is divided into three sections namely: Road Sign Detection where Colour Segmentation of the road traffic signs is carried out using HSV colour space considering varying lighting conditions and Shape Classification is achieved by using Contourlet Transform, considering possible occlusion and rotation of the candidate signs. Road Sign Tracking is introduced by using Kalman Filter where object of interest is tracked until it appears in the scene. Finally, Road Sign Recognition is carried out on successfully detected and tracked road sign by using features of a Local Energy based Shape Histogram (LESH). Experiments are carried out on 15 distinctive classes of road signs to justify that the algorithm described in this paper is robust enough to detect, track and recognize road signs under varying weather, occlusion, rotation and scaling conditions using video stream.

CORE (COnnecting REpositories)