A Framework for Real-time Traffic Sign Detection and Recognition using Grassmann Manifolds

We propose a novel, real-time framework for traffic sign detection and recognition using a camera mounted on the dashboard of a moving vehicle. Traffic sign detection and recognition plays an important role in Advanced Driver Assistance Systems (ADAS) as it helps increase driving safety. However, it is very challenging to detect and recognize traffic signs because of challenges such as perspective distortion, illumination variation, occlusion and motion blur from moving vehicle. In our framework, we use Hue Saturation Value (HSV) color filtering for traffic sign detection. In our Grassmann manifold based traffic sign recognition framework, we create subspaces of each unique traffic sign. These subspaces accommodate the uncertainties that occur during detection and variations in different instances of the same traffic sign. These subspaces lie on a Grassmann manifold and we use discriminant analysis on Grassmann manifolds for recognising them. We have carried out extensive experiments on multiple publicly available traffic sign datasets and compared our proposed framework with multiple state-of-the-art methods. Experimental results show that our system is robust and has a high degree of accuracy.