Cudalaunch nvprof1/10/2024 ![]() In: 2017 International Conference on Vision, Image and Signal Processing (ICVISP), vol. Wei, Q., Lao, S., Bai, L.: Panorama Stitching, Moving Object Detection and Tracking in UAV Videos. Teutsch, M., Krüger, W., Beyerer, J.: Moving object detection in top-view aerial videos improved by image stacking. ![]() In 2012 IEEE International Conference on Control System, Computing and Engineering, pp. 456–461 (2010)Ĭheraghi, S.A., Sheikh, U.U.: Moving object detection using image registration for a moving camera platform. In 2010 Fourth Pacific-Rim Symposium on Image and Video Technology, pp. Ibrahim, A.W.N., Ching, P.W., Seet, G.L.G., Lau, W.S.M., Czajewski, W.: Moving objects detection and tracking framework for UAV-based surveillance. Experimental results show the achieved frame processing rates of 43.1 fps, 35.5 fps and 9.1 fps for 1080p videos on Pascal, Maxwell, and Fermi microarchitectures respectively. The implementation is tested on different NVIDIA GPU microarchitectures (Fermi, Maxwell, and Pascal). ![]() This paper describes parallel implementation strategies for algorithms like feature detection, feature matching, image transformation, frame differencing, morphological processing and connected component labeling which are used to detect moving objects in UAV-sourced videos. Hence, it becomes imperative to explore parallel implementations of such algorithms using the new GPU architectures. GPU vendors regularly release newer architectures with new features to speed up various kinds of applications. However, being compute-intensive in nature, it is difficult to process high-resolution UAV-sourced videos in real-time. Real-time processing of moving object detection is required for various decision-making tasks in many of these applications. Moving object detection is an important algorithm for many such applications. Unmanned aerial vehicles (UAVs) are being increasingly used for video surveillance and remote sensing.
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