Vision-Based Incoming Traffic Estimator Using Deep Neural Network on
  General Purpose Embedded Hardware
K. G. Zoysa S. R. Munasinghe
Traffic management is a serious problem in many cities around the world. Even
the suburban areas are now experiencing regular traffic congestion.
Inappropriate traffic control wastes fuel, time, and the productivity of
nations. Though traffic signals are used to improve traffic flow, they often
cause problems due to inappropriate or obsolete timing that does not tally with
the actual traffic intensity at the intersection. Traffic intensity
determination based on statistical methods only gives the average intensity
expected at any given time. However, to control traffic accurately, it is
required to know the real-time traffic intensity. In this research, image
processing and machine learning have been used to estimate actual traffic
intensity in real time. General-purpose electronic hardware has been used for
in-situ image processing based on the edge-detection method. A deep neural
network (DNN) was trained to infer traffic intensity in each image in real
time. The trained DNN estimated traffic intensity accurately in 90% of the
real-time images during road tests. The electronic system was implemented on a
Raspberry Pi single-board computer; hence, it is cost-effective for large-scale
deployment.