Data fusion of radar and stereo vision for detection and tracking of moving objects

Detection and tracking of moving objects (DATMO) is essential for autonomous navigation systems operating in general environments. Dynamic objects must be identified, localized, and their future positions predicted to assist in decision making regarding path planning and collision avoidance. To this end, we combine information from a short range frequency modulated continuous wave (FWCW) radar and stereo vision cameras, gathered from a moving vehicle. We extract measurements from both the radar and vision subsystems, using two-dimensional Fourier analysis and sparse feature detection respectively. A segmentation of moving objects is obtained by a hierarchical clustering process, on data composed of image feature tracks and Gaussian mixtures originating from radar-based state estimation. Segmented objects are ultimately tracked using a Gaussian inverse Wishart probability hypothesis density filter (GIW-PHD). Test results on real world data suggest the novel combination of radar and vision data within the PHD filtering framework to be a viable candidate for a DATMO system.