Abstract:
Traffic and transportation play an important part in modern national economics. Efficient use of
transportation infrastructure leads to huge economic benefits. Traffic can be classified into two
main categories as homogeneous traffic and heterogeneous traffic. In transportation engineering,
sufficient, reliable, and diverse traffic data is necessary for effective planning, operations, research,
and professional practice. Even though, Intelligent Transport System are used to find answers for
that issue still it is not yet fully successful.
Many technologies have been developed to collect different types of traffic data. Traditional data
collection technologies have several drawbacks. On the other hand, video based traffic analyzing
has become popular. Computer vision techniques are used for detecting and classifying data in
traffic videos. Those technologies are highly beneficial as it can give us more information about
the parameters, easy to install and maintain and has got wide-range operation. In Computer vision,
vehicle detection process has two main steps as Hypothesis Generation (HG) and Hypothesis
Verification (HV). Background Subtraction is a popular method used in HG. There are several
algorithms used in Background Subtraction and Gaussian Mixture Model is one of them. These
methods are used in homogenous traffic situations. The objective of this study is to detect and
classify vehicles from a homogenous and heterogeneous traffic video stream using Gaussian
Mixture model.
This study was conducted using an experimental method. Several set of road traffic videos were
collected. One is collected at off peak time; i.e. 9.00am to 10.00am. At that time behavior of the
traffic is similar to homogenous traffic environment. The other set of videos is collected from
7.00am to 8.30am. At that time, road traffic has no order and the traffic density is high. It is similar
to heterogeneous traffic environment. After Gray Scaling and Noise reduction, the videos were
submitted to algorithm based on Gaussian Mixture Model. The algorithm was implemented using
Math Lab software. Vehicles are classified as large, medium and small. Manual observation results
and experiment results were compared. Accurate results were observed from homogenous traffic
conditions. But results in heterogeneous traffic conditions is less accurate. The Gaussian Mixture
Model can be used to detect vehicles in homogenous traffic conditions successfully, but it is needed
to be improved in heterogeneous traffic conditions.
