Abstract
Automatic object detection is a widely investigated problem in different fields such as military and urban surveillance. The availability of Very High Resolution (VHR) optical remotely sensed data, has motivated the design of new object detection methods that allow recognizing small objects like ships, buildings and vehicles. However, the challenge always remains in increasing the accuracy and speed of these object detection methods. This can be difficult due to the complex background. Therefore, the development of robust and flexible models that analyze remotely sensed data for vehicle detection is needed. We propose in this paper a hierarchical Bayesian model for automatic vehicle detection. Experiments performed using real data indicate the benefit that can be drawn from our approach.
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Gharbi, W., Chaari, L., Benazza-Benyahia, A. (2018). Bayesian Vehicle Detection Using Optical Remote Sensing Images. In: Blanc-Talon, J., Helbert, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2018. Lecture Notes in Computer Science(), vol 11182. Springer, Cham. https://doi.org/10.1007/978-3-030-01449-0_22
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