Implementation of road traffic signs detection based on saliency map model

In this paper, we proposed a new road traffic sign detection model based on human-like selective attention mechanism for implementing interactive workload manager system. Since the road traffic sign boards have dominant color contrast against backgrounds, we consider the color opponents and its edge information with center surround difference and normalization as a pre-processing, which is effective to intensify the sign board color characteristics as well as reduce background noise influence. After constructing the road traffic sign saliency map using the edge and color feature maps, the candidate road traffic sign regions are selected by local maximum energy searching with entropy maximization algorithm to find suitable size of the sign board areas. Computational experiment results show that the proposed model can successfully detect a road traffic sign board.