Non-parametric laser and video data fusion: Application to pedestrian detection in urban environment

In urban environments, pedestrian detection is a challenging task for automotive research, where algorithms suffer from a lack of reliability due to many false detections. This paper presents a multisensor fusion method based on a stochastic recursive Bayesian framework also called particle filter which fuses information from laser and video sensors to improve the performance of a pedestrian detection system. The main contributions of this paper are first, the use of a non-parametric data association method in order to better approximate the discrete distribution and second, the modeling of the likelihood function with a mixture of Gaussian and uniform distributions in order to take into account all the available information. Simulation results as well as results of experiments conducted on real data demonstrate the effectiveness of the proposed approach.