In recent years, the frequent appearance of obstacles on roads has been increasing. Opportune obstacle detection is crucial in driver-assistance systems to prevent traffic incidents. Artificial vision has been used to design advanced driver-assistance systems. Driver-assistance allows avoiding collisions or (mortal) accidents by offering technologies that alert the driver about potential problems. Opportune obstacle detection is an open problem in a dynamic environment; therefore, it is necessary to identify static objects and moving objects, known as obstacles, while driving a vehicle. The object identification process is mainly affected by light conditions. In this paper, we present an on-road obstacle detection system based on video analysis. The system extracts areas of interest from a video scene by using a rectangular window of observation and carrying out a sample analysis to separate the road from possible obstacles and the horizon, which is known as the segmentation process. Besides, the system calculates the obstacle trajectory by using monocular vision and an extended Kalman filter. The mechanism has been tested under several surface and lighting conditions, showing a significant improvement in terms of robustness to real world driving conditions, as compared to other state of the art methods, which are designed to work in controlled environments.