Detection and Recognition of Traffic Signs from Videos using Saliency-Enhanced Features

Paper in proceeding, 2015

Traffic sign recognition, including sign detection and classification, is an essential part in advanced driver assistance systems and autonomous vehicles. Traffic sign recognition (TSR), that exploits image analysis and computer vision techniques, has drawn increasing interest lately due to recently renewed efforts in vehicle safety and autonomous driving. Applications, among many others, include advanced driver assistance systems, sign inventory, intelligent autonomous driving. We propose efficient methods for detection and classification of traffic signs from automatically cropped street view images. The main novelties in the paper include: • An approach for automatic cropping of street view images from public available websites; The method detects and crops candidate traffic sign regions (bounding boxes) along the roads, from a specified route (i.e., the beginning and end points of the road), instead of conventionally using existing datasets; • An approach for generating saliency-enhanced features for the classifier. A novel method for obtaining the saliency-enhanced regions is proposed. It is based on a propagation process on enhancing sign part that attracts visual attention. Consequently, this leads to salient feature extraction. This approach overcomes the short coming in the conventional methods where features are extracted from the entire region of a detected bounding box which usually contains other clutter (or background). • A coarse-to-fine classification method that first classifies among different sign categories (e.g. category of forbidden, warning signs), followed by fine-classification of traffic signs within each category. The proposed methods have been tested on 2 categories of Chinese traffic signs, each containing many different signs.