![]() 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. Our experiments show that the C extension helps to identify additional parallelization opportunities and, thus, to significantly increase the performance of applications. We integrated the approach into the GCC compiler toolchain and evaluated it by running several real-world applications. These functions can then basically be ignored when the automatic parallelizer checks the parallelizability of loops. In this article, we present a seemingly simple extension to the C programming language which marks functions without side-effects. The main restriction of these tools is that the loops must be statically analyzable which, among other things, disallows function calls within the loops. Several automatic loop nest parallelizers have been developed in the past such as PluTo. To make use of this potential, an essential technique to increase the parallelism of a program is to parallelize loops. The need for parallel task execution has been steadily growing in recent years since manufacturers mainly improve processor performance by increasing the number of installed cores instead of scaling the processor’s frequency.
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