Efficient parallelization of GMM background subtraction algorithm on a multi-core platform for moving objects detection

机译：多核平台上运动物体检测的GMM背景扣除算法的高效并行化

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Gaussian Mixture Model background subtraction (GMM) method is nowadays used in many moving object detection applications. This common approach is performed statistically on each single pixel in the captured frames. Thus, it is well suitable for parallel processing. With the great evolution of multi-core platforms, the parallelization of this algorithm is the most efficient way for its real-time acceleration. In this paper, we propose an efficient multi-threading parallelization of GMM on a 16-cores Intel node using the OpenMP framework. This is carried out by removing data dependencies between different threads which slows down the system; balancing their computational load and avoiding some hidden errors when measuring the performance. The use of a suitable compile environment and options showed that high scalability and linear speedup are achieved even when high number of cores is used.

机译：如今，高斯混合模型背景减法（GMM）方法被用于许多运动物体检测应用中。对捕获的帧中的每个单个像素进行统计上执行此通用方法。因此，它非常适合并行处理。随着多核平台的飞速发展，该算法的并行化是实现其实时加速的最有效方法。在本文中，我们建议使用OpenMP框架在16核Intel节点上进行GMM的高效多线程并行化。这是通过消除不同线程之间的数据依赖关系来进行的，这会减慢系统速度;平衡其计算负荷，并避免在测量性能时出现一些隐藏的错误。使用合适的编译环境和选项表明，即使使用大量内核，仍可以实现较高的可伸缩性和线性加速。

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《International Conference on Advanced Technologies for Signal and Image Processing》|2018年|1-5|共5页
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Lhoussein Mabrouk; Sylvain Huet; Dominique Houzet; Said Belkouch; Abdelkrim Hamzaoui; Yahya Zennayi;
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Instruction sets; Scalability; Multicore processing; Task analysis; Real-time systems; Object detection;

机译：指令集;可扩展性;多核处理;任务分析;实时系统;目标检测;

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专利

1. Design and Implementation of High Speed Background Subtraction Algorithm for Moving Object Detection [J] . Sunanda R. Hanchinamani, Sayantam Sarkar, Satish S. Bhairannawar Procedia Computer Science . 2016,第1期

机译：运动目标检测高速背景减法算法的设计与实现
2. BSFD: BACKGROUND SUBTRACTION FRAME DIFFERENCE ALGORITHM FOR MOVING OBJECT DETECTION AND EXTRACTION [J] . D STALIN ALEX, Dr. AMITABH WAHI Journal of Theoretical and Applied Information Technology . 2014,第3期

机译：BSFD：背景减法帧差分算法，用于移动对象检测和提取
3. Moving Object Detection With a Freely Moving Camera via Background Motion Subtraction [J] . Yuanyuan Wu, Xiaohai He, Truong Q. Nguyen IEEE Transactions on Circuits and Systems for Video Technology . 2017,第2期

机译：通过背景运动减法使用自由移动的摄像机检测运动对象
4. Efficient parallelization of GMM background subtraction algorithm on a multi-core platform for moving objects detection [C] . Lhoussein Mabrouk, Sylvain Huet, Dominique Houzet, International Conference on Advanced Technologies for Signal and Image Processing . 2018

机译：GMM背景减法算法对移动物体检测的多核平台上的高效并行化
5. A simple and computationally efficient Fourier transform based algorithm for moving target detection in noisy background with no a priori target information. [D] . Ouattara, Siendou. 2002

机译：一种简单且计算有效的基于傅立叶变换的算法，用于在无先验目标信息的嘈杂背景下进行运动目标检测。
6. Moving Object Detection under a Moving Camera via Background Orientation Reconstruction [O] . Wenlong Zhang, Xiaoliang Sun, Qifeng Yu 2020

机译：通过背景定向重建在运动相机下的运动物体检测
7. Implementation of moving object detection using angle change method This paper conclude the work in terms of input and output parameter that has been considered while detecting the moving object in video surveillance. It also provides with a look up of our work area. This paper presents a discussion of various techniques like Background subtraction, Block matching, Frame differencing, Optical Flow used in motion detection and also include the comparative analysis of two algorithms first is Colour method(Space vector difference method) and second is angle change method. [O] . 2017

机译：使用角度变化方法的移动物体检测的实现本文在检测视频监控中检测到移动对象的同时考虑的输入和输出参数方面的工作结束。它还提供了我们工作区的抬头。本文提出了与背景减法，块匹配，帧差异，在运动检测中使用的光流等来的各种技术的讨论，并且还包括两个算法的比较分析首先是颜色方法（空间向量差法）和第二是角度变化方法。
8. Parallel Flux Tensor Analysis for Efficient Moving Object Detection. [R] . Palaniappan, K., Ersoy, I., Seetharaman, G., 2011

机译：用于高效运动物体检测的并行磁通张量分析。

Efficient parallelization of GMM background subtraction algorithm on a multi-core platform for moving objects detection

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