Vehicle detection from high resolution aerial images has been studied for many years. However, a robust and efficient vehicle detection is still challenging. In this paper, a novel and robust method for automatic vehicle detection from aerial images was presented. In this method, a GIS road vector map is used to constrain a vehicle detection system to parking lot networks, edge detection and morphological preprocessing method are used to identify candidate vehicle pixels. Different types of vehicle templates are selected to adaptively detect the similar vehicles by their correlation coefficient with the same size of the window. Experiment was conducted using 0.15 meter resolution aerial images, the result demonstrated that the new method had an excellent detection performance.
With faster hardware and algorithmic improvements, real-time ray tracing is finally within reach. A variety of different data structures have been developed for ray tracing over the past decades. These spatial data structures crucial for fast ray tracing must be rebuilt or updated as the scene changes, and this can become a bottle-neck for the speed of ray tracing. In this paper, a novel algorithm is proposed to improve the rendering speed for static and moving objects by build kd-trees respectively, i.e., we construct multiple kd-trees. This building method allows for ray tracing animations without rebuilding the spatial index structures for the whole scene, just for dynamic objects. We implement the proposed algorithm for ray tracing dynamic scene on CPU+GPU platform, resulting in frame rates of 3~5 fps for 1024*1024 images, which improves rendering speed of dynamic scenes significantly.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.