According to a report, night time and poor illumination driving is overall 2-3 times more dangerous then day time. For example, young people aged 18-24 were killed between 21:00 and 05:59 (the night-time and early morning) on week-days in the EU-23 countries because of road accident in 2010. As the similar pattern with EU, most pedestrian-vehicle accidents occur between 6 p.m. and 8 a.m., and the rate of pedestrian fatalities is highest between 4 a.m. and 6 a.m. in South Korea. Among several factors such as inebriated drivers and pedestrian, drowsiness, decreased visibility is the major cause of pedestrian-vehicle accident at night. To reduce the accident owing to driver's inattention at night, recent advanced driver assistance system (ADAS) has been researching on automatic pedestrian detection and tracking using night vision camera. Therefore, this tutorial focuses on introducing a multiple pedestrians tracking system using a thermal camera that is able to discern thermal energy at night-time. In a pedestrian tracking-by-detection system, multi-pedestrian detection accuracy is essential for post tracking process. Since the temperature difference between the pedestrian and background depends on the season and weather, we therefore first introduce two models for detecting pedestrians according to the season and weather, which are determined using Weber-Fechner's law. Two detection models use the optimal levels of the image scaling and search area instead of image pyramid to reduce the computational cost of image scaling for detecting multiple pedestrians of various sizes. Online learning is appropriate in the case that image frames is obtained sequentially. Theoretically offline learning could obtain global optimal solution while it is not as practical as online learning. Therefore, we introduce some state-of-the-art real-time online learning algorithms with our online learning based on boosted random ferns (BRFs) in detail based on the references as the second topi...