Frequency-Tuned Taillight-Based Nighttime Vehicle Braking Warning System

Advanced vehicle safety is a critical issue in recent years for automobiles, especially when the number of vehicles is growing rapidly worldwide. The decreasing cost of cameras makes it feasible to have an intelligent system of visually based event detection in front for forward collision avoidance and mitigation. When driving at night, vehicles in front are generally visible by their taillights. The brake lights are particularly important because drivers need to focus on them. 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 domains. Unlike the traditional approaches that employ the knowledge of the heuristic features, such as symmetry and position of rear facing vehicle, size, and so on, we focus on finding the invariant features from the regions of brake lights in the frequency domain and therefore can conduct the detection process in a part-based manner. Experiments with an extensive dataset show 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.