Improving light spot tracking for an Automatic Headlight Control Algorithm

Abstract

Multi target tracking is an important task for an Automatic Headlight Control Algorithm(AHC). The task is challenging due to the presence of small, closely spaced light spots and the limited computing power of an embedded platform. Thus the tracking method has to be accurate and at the same time computationally efficient. This paper presents a novel method which achieves this task by combining the concepts of interest point tracking and position tracking. In interest point tracking, points are tracked using appearance based features while position tracking makes use of kinematic features. The interest point tracking method in this paper employs a feature set, which consists of well known appearance based features along with a novel light spot environmental feature. A genetic algorithm based search method was used to filter out this feature set from a bigger set. The information from these features is combined with the kinematic features using a computationally efficient method. This fused information is used to track light spots. The new method improved the system performance by reducing the tracking failures by 65% and showed better performance during worst cases like vehicle pitching.