Vehicle Rear-Lamp Detection at Nighttime via Probabilistic Bitwise Genetic Algorithm

Abstract

Rear-lamp detection of a vehicle at nighttime is an important technique for advanced driver-assistance systems. We present a detection method by employing a variant of genetic algorithm, which utilizes bitwise genetic operation instead of classic crossover and mutation. That is, the detection task is cast to a localization problem under an evolutionary optimization framework. Specifically, geometric parameters of a rectangle pair form a model to represent the detected rear-lamp pair. The fitness function for evaluating each candidate solution is combinatorial, which consists of multiple fitness functions designed under handcrafted rules from the observation. In addition, the solution space is narrowed down by extracting the red-light sources, which yields in more efficient solution exploration. Experiment with a publicly available dataset which involves images captured in various traffic situations shows the effectiveness of our method qualitatively and quantitatively.