The Pedestrian Detection System (PDS) has become a significant area of research designed to protect pedestrians. Despite the huge number of research work, the most current PDSs are designed to detect pedestrians without knowing their distances from cars. In fact, a priori knowledge of the distance between a car and pedestrian allows this system to make the appropriate decision in order to avoid collisions. Typical methods of distance measurement require additional equipment (e.g., Radars) which, unfortunately, cannot identify objects. Moreover, traditional stereo-vision methods have poor precision in long-range conditions. In this thesis, we use the keypoint-based feature extraction method to generate the parallax in a binocular vision system in order to measure a detectable object; this is used instead of a disparity map. Our method enhances the tolerance to instability of a moving vehicle; and, it also enables binocular measurement systems to be equipped with a zoom lens and to have greater distance between cameras. In addition, we designed a crossover re-detection and tracking method in order to reinforce the robustness of the system (one camera helps the other reduce detection errors). Our system is able to measure the distance between cars and pedestrians; and, it can also be used efficiently to measure the distance between cars and other objects such as Traffic signs or animals. Through a real word experiment, the system shows a 7.5% margin of error in outdoor and long-range conditions.