Evolutionary Rear-Lamp Tracking at Nighttime

Rear-lamp tracking at nighttime plays a momentous role in the advanced driver assistance system (ADAS), involving collision mitigation, automatic cruise control, automatic headlamp dimming, etc. Most of the existing tracking methods based on monocular camera leverage on color features. However, such tracking methods can be easily influenced by background clutter, illumination change, distance variation, and occlusion. In this paper, we propose an evolutionary adaptive rear-lamp tracking method at nighttime, in which a novel genetic algorithm powered by the probabilistic bitwise operation (PBO) is utilized. Also, to improve the robustness against various environments, a balanced fitness function is proposed by taking color information, symmetry, spatial relationship, and rigidity into account. Especially, a series of adaptive thresholds based on rear data in HSV color space is proposed to exploit color information reasonably with respect to our task. A strategy to deal with occlusion is also proposed, which relies on color information and rigidity. Moreover, to our knowledge, there is no publicly available dataset for rear-lamp tracking at nighttime. To fill the gap between the real-world application and the theoretical research, we create a novel dataset, which contains diverse traffic conditions at nighttime. The experimental results indicate that our method outperforms comparative online tracking methods in terms of success rate and center location error.