Abstract

Vision based traffic scene perception (TSP) is one of many fast-emerging areas in the intelligent transportation system. This field of research has been actively studied over the past decade. TSP involves three phases: detection, recognition and tracking of various objects of interest. Since recognition and tracking often rely on the results from detection, the ability to detect objects of interest effectively plays a crucial role in TSP. The aim of traffic sign detection is to alert the driver of the changed traffic conditions. The task is to accurately localize and recognize road signs in various traffic environments. Prior approaches use colorant shape information. However, these approaches are not adaptive under severe weather and lighting conditions. Additionally, appearance of traffic signs can physically change over time, due to the weather and damage caused by accidents. Instead of using color and shape features, most recent approaches employ texture or gradient features, such as local binary patterns and histogram of oriented gradients. These features are partially invariant to image distortion and illumination change, but they are still unable to handle severe deformations.