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首页> 外文期刊>Information Sciences: An International Journal >A novel GPU-aware Histogram-based algorithm for supporting moving object segmentation in big-data-based IoT application scenarios
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A novel GPU-aware Histogram-based algorithm for supporting moving object segmentation in big-data-based IoT application scenarios

机译：一种基于GPU的GPU感知直方图的基于大数据的IOT应用场景中的移动对象分割的新型GPU感知直方图算法

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摘要

Multimedia data are a popular case of Big Data that expose the classical 3V characteristics (i.e., volume, velocity and variety). Such kind of data are likely to be processed within the core layer of Internet of Things (IoT) platforms, where a multiple, typically high, number of "things" (e.g., sensors, devices, actuators, and so forth) collaborate to massively process big data for supporting intelligent algorithms running over them. In such platforms, the computational bottleneck is very often represented by the component running the main algorithm, while communication and cooperation costs still remain relevant. Inspired by this emerging trend of big-data-based IoT applications, in this paper we focus on the specific application context represented by the problem of supporting moving object segmentation over images originated in the context of big multimedia data, and we propose an innovative background maintenance approach to this end. In particular, we provide a novel GPU-aware Histogram-based Moving Object Segmentation algorithm that adopts a pixel-oriented approach and it is based on Graphic Processing Units (GPU), called PixHMOS_GPU. PixH-MOS_GPU allows us to achieve higher performance, hence making the computational gap of big-data-based IoT applications decisively smaller. Experimental results clearly confirm our arguments. (C) 2019 Published by Elsevier Inc.
机译：多媒体数据是一个流行的大数据情况，暴露经典的3V特征（即，体积，速度和品种）。这种数据可能会在物联网（物联网）平台的核心层内处理，其中多个通常高，数量的“东西”（例如，传感器，设备，致动器等）与大规模合作处理支持智能算法的大数据。在这种平台中，计算瓶颈通常由运行主算法的组件表示，而通信和合作成本仍然保持相关。通过这种基于大数据的IOT应用程序的新兴趋势的启发，本文专注于支持在大多媒体数据的上下文中源自图像的支持对象分割问题的特定应用上下文，并提出了一个创新背景此目的的维护方法。特别地，我们提供了一种新的GPU感知直方图的基于直方图的移动对象分割算法，其采用以像素为导向的方法，并且它基于称为Pixhmos_GPU的图形处理单元（GPU）。 PIXH-MOS_GPU允许我们实现更高的性能，因此使基于大数据的IOT应用程序的计算间隙果断更小。实验结果明确证实了我们的论点。 （c）2019由elsevier公司出版

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