Affiliations: School of Computer and Communication Eng., Daegu University, Gyeongsan, South Korea
Correspondence:
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Corresponding author. Seokwon Yeom, School of Computer and Communication Eng., Daegu University, Gyeongsan, 38453 South Korea. E-mail: yeom@daegu.ac.kr.
Abstract: Recently, drone technology has developed rapidly for various purposes. A drone is very useful for aerial surveillance due to its remote sensing capability. Multiple target detection and tracking are essential to recognize any harmful threats in advance, however the image captured at a distance is easily degraded due to blurring and noise as well as low resolution. This paper addresses the detection and tracking of moving vehicles with drone imaging. A drone captures video sequences of multiple moving vehicles from a distance. Cars and buses are the objects of interests driving on urban roads. The detection step consists of frame difference followed by thresholding and morphological operation considering the size of region of interest (ROI). The centroids of the ROI’s are considered measurements for tracking. Tracking is performed with interacting multiple model (IMM) filtering, which estimate the state of vectors and covariance matrices using multiple modes of Kalman filtering. The measurements in the validation region are associated with established tracks by the nearest neighbor rule. In the experiment, total seven moving cars and buses are captured at a long distance by a drone. It will be shown that the proposed method well detects the moving vehicles and achieves a good accuracy in estimating their locations.
