One benefit of employing computer vision techniques to extract individual vehicles from a highway traffic scene
is the abundance of networked, traffic surveillance cameras that may be leveraged as the input video. However,
the acquisition sensors that are monitoring the highway traffic will have very limited quality. Additionally, video
streams are heavily compressed, causing noise and, in some cases, visible artifacts to be introduced into the
video. Further challenges are presented by external environmental and weather conditions, such as rain, fog,
and snow, that cause video blurring or noise. The resulting output of a segmentation algorithm yields poorer
results, with many vehicles undetected or partially detected. Our goal is to extract individual vehicles from a
highway traffic scenes using super-resolution and the utilization of Gaussian mixture model algorithm (GMM).
We used a speeded-up enhanced stochastic Wiener filter for SR reconstruction and restoration. It can be used to
remove artifacts and enhance the visual quality of the reconstructed images and can be implemented efficiently
in the frequency domain. The filter derivation depends on the continuous-discrete-continuous (CDC) model that
represents most of the degradations encountered during the image-gathering and image-display processes. Then,
we use GMM followed by the clustering of individual vehicles. Individual vehicles are detected from the segmented
scene through the use of a series of morphological operations, followed by two-dimensional connected component
labeling. We evaluate our hybrid approach quantitatively in the segmentation of the extracted vehicles.