An Appearance-based Approach to Detect the Wrong-way Movement of Vehicles Using Deep Convolutional Neural Network
Mutasim Billah Bin Ahmad, Md. Rafsan Jany Chowdhury, Akif Ahmed, Kashfa Sehejat Sezuti, Tohedul Islam · 2020
To guarantee the enforcement of traffic rules, the identification of traffic rule violators is an exceptionally alluring yet difficult assignment to implement and the detection of the wrong-way movement of vehicles is one of them. In this paper, an appearance-based approach is proposed which detects the front and back side of the vehicles on a highway with the help of a deep convolutional neural network and decides whether a vehicle is moving along the wrong-way or not based on the user expectation to see the side of a vehicle on each side of the highway using a handcrafted region divider algorithm. The effectiveness of this strategy has been assessed on a primary data-set built on real-time traffic videos captured from several significantly busy highways of Dhaka Metropolitan City and proven quite productive with an accuracy of 96% on successful detection of wrong-way movement of vehicles.