Visual feature tracking based on PHD filter for vehicle detection

Vehicle detection is one of the classical application among the Advance Driver Assistance Systems (ADAS). Applications like emergency braking or adaptive cruise control (ACC) require accurate and reliable vehicle detection. In latest years the improvements in vision detection have lead to the introduction of computer vision to detect vehicles by means of these more economical sensors, with high reliability. In the present paper, a novel algorithm for vehicle detection and tracking based on a probability hypothesis density (PHD) filter is presented. The first detection is based on a fast machine learning algorithm (Adaboost) and Haar-Like features. Later, the tracking is performed, by means features detected within the bounding box provided by the vehicle detection. The features, are tracked by a PHD filter. The results of the features being tracked are combined together in the last step, based on several different methods. Test provided show the performance of the PHD filter in public sequences using the different methods proposed.