American Journal of Applied Sciences

Vehicle Detection in Aerial Traffic Monitoring

Dmitry Sincha, Mikhail Chervonenkis and Pavel Skribtsov

DOI : 10.3844/ajassp.2016.46.54

American Journal of Applied Sciences

Volume 13, Issue 1

Pages 46-54


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.


© 2016 Dmitry Sincha, Mikhail Chervonenkis and Pavel Skribtsov. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.