PHD filter for vehicle tracking based on a monocular camera

Novel advance driver assistance systems, such as emergency braking and adaptive cruise control require the most reliable detection algorithms. Furthermore, in the recent years, the use of computer vision approaches in these type of applications is becoming more frequent. However, when dealing with these technologies, reliability is a very important factor that still requires improvement. On this paper, it is presented a tracking algorithm which aims in improving the accuracy of these applications, based on computer vision and modern Probability Hypothesis Density (PHD) Filter technique. The tracking is performed on the features detected within the bounding box provided by a computer video based vehicle detection algorithm. The features tracked are combined in a last stage, providing accurate monocular camera tracking. Test provided, allowed to identify the best method for feature combination. Furthermore, it was proved that under the proper visibility conditions, the PHD filter design is able to improve current methods such as Unscented Kalman Filter.