  In recent decades, due to the groundbreaking improvements in machine vision,
many daily tasks are performed by computers. One of these tasks is
multiple-vehicle tracking, which is widely used in different areas such as
video surveillance and traffic monitoring. This paper focuses on introducing an
efficient novel approach with acceptable accuracy. This is achieved through an
efficient appearance and motion model based on the features extracted from each
object. For this purpose, two different approaches have been used to extract
features, i.e. features extracted from a deep neural network, and traditional
features. Then the results from these two approaches are compared with
state-of-the-art trackers. The results are obtained by executing the methods on
the UA-DETRACK benchmark. The first method led to 58.9% accuracy while the
second method caused up to 15.9%. The proposed methods can still be improved by
extracting more distinguishable features.
