Research Article Open Access

Vehicle Detection in Aerial Traffic Monitoring

Dmitry Sincha1, Mikhail Chervonenkis1 and Pavel Skribtsov1
  • 1 PAWLIN Technologies Ltd, Dubna, Russia


This work describes a cascade detection of vehicles in Unmanned Aerial Vehicle (UAV) images and videos. There are some new approaches used in the detection. In particular, the Region of Interest (ROI) search is not only based on GIS and navigation data, but also employs visual method based on rapid image segmentation and road detection. The work also suggests doing ROI segmentation by the superpixel technique and trainable four-level cascade detector that uses artificial neural networks as classifiers. Characteristics of the being analyzed regions (combined superpixels) are based on geometric and texture features, as well as on deep features extracted from the image patches by nonlinear auto encoders. To improve the detection quality of the moving vehicles a separate stage of the detector based on optical flow analysis was introduced. Proposed detection algorithm was benchmarked on the real UAV videos and showed the sufficiently high accuracy. Performance of the algorithm allows supposing the on-board usage.

American Journal of Applied Sciences
Volume 13 No. 1, 2016, 46-54


Submitted On: 23 September 2015 Published On: 11 January 2016

How to Cite: Sincha, D., Chervonenkis, M. & Skribtsov, P. (2016). Vehicle Detection in Aerial Traffic Monitoring. American Journal of Applied Sciences, 13(1), 46-54.

  • 2 Citations



  • Unmanned Aerial Vehicles (UAV)
  • Vehicle Detection
  • Road Detection
  • Superpixel
  • Deep Learning