Rear-Lamp Vehicle Detection and Tracking in Low-Exposure Color Video for Night Conditions

Automated detection of vehicles in front is an integral component of many advanced driver-assistance systems (ADAS), such as collision mitigation, automatic cruise control (ACC), and automatic headlamp dimming. We present a novel image processing system to detect and track vehicle rear-lamp pairs in forward-facing color video. A standard low-cost camera with a complementary metal-oxide semiconductor (CMOS) sensor and Bayer red-green-blue (RGB) color filter is used and could be utilized for full-color image display or other color image processing applications. The appearance of rear lamps in video and imagery can dramatically change, depending on camera hardware; therefore, we suggest a camera-configuration process that optimizes the appearance of rear lamps for segmentation. Rear-facing lamps are segmented from low-exposure forward-facing color video using a red-color threshold. Unlike previous work in the area, which uses subjective color threshold boundaries, our color threshold is directly derived from automotive regulations and adapted for real-world conditions in the hue-saturation-value (HSV) color space. Lamps are paired using color cross-correlation symmetry analysis and tracked using Kalman filtering. A tracking-based detection stage is introduced to improve robustness and to deal with distortions caused by other light sources and perspective distortion, which are common in automotive environments. Results that demonstrate the system's high detection rates, operating distance, and robustness to different lighting conditions and road environments are presented.