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. PIXHMOS_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.
