Algorithms in the field of driver assistance have been limited by their requirement for real time in the initial phase of their development. However, as computing power is increasing steadily, new possibilities arise. With focus on this situation a review is presented not defined by its designated field of application in driver assistance systems, but rather by the methods in use, namely video-based object recognition using machine learning. Recent methods are compared in a highly summarized table using criteria such as recognition rate, computational requirements or number of training samples required. Concluding their potential use in driver assistance is discussed.