Traffic accidents are more frequent during night-time despite lower traffic volume. Driver assistance systems can increase car and road safety. Such systems are often visual based, where the base is vehicle light detection. A method for night-time vehicle light detection is presented which is capable to detect front and rear vehicle lights. The detection procedure operates on raw pixels, which does not need extensive image pre-processing such as image segmentation thus improving robustness and performance. Although two classes of vehicle lights are detected, a single binary classifier architecture is used to detect all categories of vehicles lights. The obtained detections are then labeled by using the same approach. The result is a simple yet effective architecture capable to detect vehicle lights in various night-time lighting conditions.
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- A Unified Approach for On-Road Visual Night-Time Vehicle Light Detection
- verfasst von
- Springer International Publishing