Vehicle Detection in Aerial Traffic Monitoring | Science Publications

American Journal of Applied Sciences

Dmitry Sincha¹  Pavel Skribtsov¹  Mikhail Chervonenkis¹ 

关键词: Unmanned Aerial Vehicles (UAV);    Vehicle Detection;    Road Detection;    Superpixel;    Deep Learning;   

DOI  :  10.3844/ajassp.2016.46.54

学科分类：自然科学（综合）

来源: Science Publications

【 摘 要 】

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.

【 授权许可】

Unknown