  This paper introduces a novel perception framework that has the ability to
identify and track objects in autonomous vehicle's field of view. The proposed
algorithms don't require any training for achieving this goal. The framework
makes use of ego-vehicle's pose estimation and a KD-Tree-based segmentation
algorithm to generate object clusters. In turn, using a VFH technique, the
geometry of each identified object cluster is translated into a multi-modal PDF
and a motion model is initiated with every new object cluster for the purpose
of robust spatio-temporal tracking. The methodology further uses statistical
properties of high-dimensional probability density functions and Bayesian
motion model estimates to identify and track objects from frame to frame. The
effectiveness of the methodology is tested on a KITTI dataset. The results show
that the median tracking accuracy is around 91% with an end-to-end
computational time of 153 milliseconds
