  The rapid advancement in the field of deep learning and high performance
computing has highly augmented the scope of video based vehicle counting
system. In this paper, the authors deploy several state of the art object
detection and tracking algorithms to detect and track different classes of
vehicles in their regions of interest (ROI). The goal of correctly detecting
and tracking vehicles' in their ROI is to obtain an accurate vehicle count.
Multiple combinations of object detection models coupled with different
tracking systems are applied to access the best vehicle counting framework. The
models' addresses challenges associated to different weather conditions,
occlusion and low-light settings and efficiently extracts vehicle information
and trajectories through its computationally rich training and feedback cycles.
The automatic vehicle counts resulting from all the model combinations are
validated and compared against the manually counted ground truths of over 9
hours' traffic video data obtained from the Louisiana Department of
Transportation and Development. Experimental results demonstrate that the
combination of CenterNet and Deep SORT, Detectron2 and Deep SORT, and YOLOv4
and Deep SORT produced the best overall counting percentage for all vehicles.
