Vehicle detection and traffic density monitoring from very high resolution satellite video data

Abstract

In this paper an automated vehicle detection and traffic density estimation algorithm has been developed and validated for very high resolution satellite video data. The algorithm is based on an adaptive background estimation procedure followed by a background subtraction at every video frame. The vehicle detection is performed through a further mathematical morphology and statistical analysis on the computed connected components. The traffic density has been estimated based on a lower resolution grid superimposed on the scene. In particular, at every subregion the number of the detected vehicles is calculated and the density is then estimated for the entire road network at every frame. The developed algorithm has been quantitatively evaluated. The quite promising results indicate the potentials of the proposed approach, while parallel GPU implementations can allow for real-time performance.