The number of vehicles in circulation in modern urban centers has greatly increased, which motivates the development of automatic traffic monitoring systems. Consequently, camera-based traffic monitoring systems are becoming more widely used, since they offer important technological advantages in comparison with traditional traffic monitoring systems (e.g., simpler maintenance and more flexibility for the design of practical configurations). The segmentation of the foreground (i.e., vehicles) is a fundamental step in the workflow of a camera-based traffic monitoring system. However, foreground segmentation can be negatively affected by vehicle shadows. This paper discusses the types of shadow detection methods available in the literature, their advantages, disadvantages, and in which situations these methods can improve camera-based vehicle detection for traffic monitoring. In order to compare the performance of these different types of shadow detection methods, experiments are conducted with typical methods of each category using publicly available datasets. This work shows that shadow detection definitely can improve the reliability of traffic monitoring systems, but the choice of the type of shadow method depends on the system specifications (e.g., tolerated error), the availability of computational resources, and prior information about the scene and its illumination in regular operation conditions.