Video Based Vehicle Counting Using Deep Learning Algorithms

Abstract

Traffic density in roads has been increasing day by day which needs intelligent transportation system that can handle the traffic. Traffic management has become inevitable for smart cities. The enormous increase in vehicle numbers has generated more pressure to manage traffic congestion especially during peak hours. If the traffic congestion at a particular point of time can be found, then that information can be useful for managing the traffic in different lanes and change the traffic light cycle dynamically according to the vehicle count in different lanes. In recent years video surveillance and monitoring has been gaining importance. Video can be analyzed which can be used to find the traffic density. Many useful information can be obtained by video processing like real time traffic density. Vehicle counting can be done by detecting the object, tracking it and then finally counting the objects. Many different techniques are available for object detection and tracking. Deep learning techniques for object detection led to remarkable improvements compared to conventional image processing techniques by removing the weakness in the conventional techniques. This paper provides a survey on various techniques available for vehicle detection and tracking.

Original document

The different versions of the original document can be found in:

• https://ieeexplore.ieee.org/ielx7/9058619/9074154/09074280.pdf

• http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=9074280,

http://xplorestaging.ieee.org/ielx7/9058619/9074154/09074280.pdf?arnumber=9074280,

https://academic.microsoft.com/#/detail/3019299928

• http://xplorestaging.ieee.org/ielx7/9058619/9074154/09074280.pdf?arnumber=9074280,

http://dx.doi.org/10.1109/icaccs48705.2020.9074280