Frequency-Tuned Nighttime Brake-Light Detection

Advanced safety vehicle (ASV) is a critical issue in recent years for automobiles, especially when the number of vehicles is growing rapidly world wide. The cost down of general cameras makes it feasible to having an intelligent system of visual-based event detection in front for forward collision avoidance and mitigation. When driving in nighttime, vehicles in front are generally visible by their tail and brake lights. The brake lights are particularly important due to their consequent events that drivers need to focus on. Therefore, in this paper, we propose a novel approach that can detect brake lights at night using a camera by analyzing the signal in both spatial and frequency domain. Unlike the traditional approaches that employ the knowledge of the heuristic features, such as symmetry and position of rear facing vehicle, size and so forth, we focus on finding the invariant features from the regions of brake lights in frequency domain and therefore can conduct the detection process in a part-based manner. Experiment from extensive dataset shows that our proposed system can efficiently and effectively detect brake lights under different lighting and traffic conditions, and thus prove its feasibility in real-world environments.