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Vehicle Classification For Automatic Traffic Density Estimation
Abdelhady, Aya
Abstract:
Automatic traffic light control at intersection has recently become one
of the most active research areas related to the development of intelligent
transportation systems (ITS). Due to the massive growth in urbanization and
traffic congestion, intelligent vision based traffic light controller is needed to
reduce the traffi c delay and travel time especially in developing countries as
the current automatic time based control is not realistic while sensor-based
tra ffic light controller is not reliable in developing countries.
Vision based traffi c light controller depends mainly on traffic congestion
estimation at cross roads, because the main road junctions of a city are these
roads where most of the road-beds are lost. Most of the previous studies
related to this topic do not take unattended vehicles into consideration when
estimating the tra ffic density or traffi c flow. In this study we would like to
improve the performance of vision based traffi c light control by detecting
stationary and unattended vehicles to give them higher weights, using image
processing and pattern recognition techniques for much e ffective and e ffecient
tra ffic congestion estimation.
Advisor:Mohamed, Moustafa
Committe Member:Gonied, Amr , Balasa, Flourin
Department:American University in Cairo. Dept. of Computer Science and Engineering