A Binary-Directional Shape Descriptor for the Enhancement of Real-Time Automatic Headlight Control Function

Abstract

Advanced Driver Assistance Systems (ADAS) are becoming more and more important in recent years. All relevant manufacturers equip their cars with various safety features and in some locations it is even mandatory to do so. One of such systems is Automatic Headlight Control (AHC) algorithm that prevents from accidental dazzling of other road users. Recognizing vehicle lights with high accuracy is crucial in this algorithm. Any improvement can result in the limitation of car accidents and dangerous situations on the road. The paper introduces a novel visual descriptor that describes previously binarized blob shapes of the analyzed objects. We also present our referenced AHC system that operates not only on the images but also on the set of other visual features. Adding new feature leads to the improvement of around 1.5 percentage point of vehicle lights classification accuracy (21% reduction of incorrectly classified light sources). It can also replace the already used features (four-time reduction from 32 to 8 feature values) and keep the classification accuracy at the similar level. The proposed solution has been tested with an extensive set of video sequences (total duration exceeding ten hours) under diverse weather conditions and from different types of roads. The proposed descriptor is shown to be far less computationally complex, and it is proved that it can outperform other state-of-the-art descriptors when analyzed individually.

Keywords

• Adaptive headlight control
• automatic headlight control
• computer vision
• high beam control
• night time vehicle detection
• shape description