The Advanced Driver Assistance Systems, known as ADAS, require algorithms able to detect and identify the different users in the road. Due to the demanding requirements of these applications, these algorithms should be reliable and precise. Such tasks are difficult to be accomplished by a single sensor, thus the fusion of different data sources is mandatory in order to fulfill these strong requirements.The present work proposes a data fusion system, based on two common sensors in intelligent transport systems (scanner laser and computer vision). The system, based on high level data fusion, detects pedestrians using each sensor independently, and information is fused later using highly efficient tracking and estimation algorithms.The first step, based on laser scanner, detects pedestrians using a pattern matching approach. Later, by means of Histogram of Oriented Gradients (HOG) algorithm, pedestrians are detected based on computer vision. Finally, both detections are fused at high level, and the movement of the pedestrians is estimated according to both Kalman Filter (KF) and Unscentered Kalman Filter (UKF) approaches.