Robust multiple car tracking with occlusion reasoning
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
SIGGRAPH '78 Proceedings of the 5th annual conference on Computer graphics and interactive techniques
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
On-Road Vehicle Detection: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the conference on Design, automation and test in Europe
WSEAS Transactions on Computers
PixNet: interference-free wireless links using LCD-camera pairs
Proceedings of the sixteenth annual international conference on Mobile computing and networking
Salient video cube guided nighttime vehicle braking event detection
Journal of Visual Communication and Image Representation
Visual-based spatiotemporal analysis for nighttime vehicle braking event detection
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
A vision-based blind spot warning system for daytime and nighttime driver assistance
Computers and Electrical Engineering
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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.