Algorithm and architecture co-design of Mixture of Gaussian (MoG) background subtraction for embedded vision

Tabkhi, Hamed; Bushey, Robert; Schirner, Gunar

doi:10.1109/acssc.2013.6810615

Cited by 9 publications

(4 citation statements)

References 8 publications

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“…X and Y in the boundary are found. Mixed Gaussian (MoG) [15] uses K Gaussian components, each with a weight w i,t , an intensity mean u i,t , and a standard deviation σ i,t…”

Section: Image Processingmentioning

confidence: 99%

A Visual Human-Computer Interaction System Based on Hybrid Visual Model

Chen

Wang

et al. 2022

Security and Communication Networks

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The traditional human-computer interaction is mainly through the mouse, keyboard, remote control, and other peripheral equipment electromagnetic signal transmission. This paper aims to build a visual human-computer interaction system through a series of deep learning and machine vision models, so that people can achieve complete human-computer interaction only through the camera and screen. The established visual human-computer interaction system mainly includes the function modes of three basic peripherals in human-computer interaction: keyboard, mouse (X-Y position indicator), and remote control. The convex hull method was used to switch between these three modes. After issuing the mode command, Gaussian mixture was used to quickly identify the moving human body to narrow the scope of our image processing. Subsequently, finger detection in human body was realized based on the Faster-RCNN-ResNET50-FPN model structure, and realized the function of moving mouse and keyboard through the relationship between different fingers. At the same time, the recognition of human body posture was done by using MediaPipe BlazePose, and the action classification models were established through the Angle between body movements so as to realize the control function of remote control. In order to ensure the real-time performance of the interactive system, according to the characteristics of different data processing processes, CPU and GPU computing power resources are used to cross-process images to ensure the real-time performance. The recognition accuracy of the human-computer interaction system is above 0.9 for the key feature points of human body, and above 0.87 for the recognition accuracy of four kinds of command actions. It is hoped that vision-based human-computer interaction will become a widely used interaction mode in the future.

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“…X and Y in the boundary are found. Mixed Gaussian (MoG) [15] uses K Gaussian components, each with a weight w i,t , an intensity mean u i,t , and a standard deviation σ i,t…”

Section: Image Processingmentioning

confidence: 99%

A Visual Human-Computer Interaction System Based on Hybrid Visual Model

Chen

Wang

et al. 2022

Security and Communication Networks

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“…For example, Goyal and Singhai [120] evaluated six improvements of MOG on the CDnet 2012 dataset showing that Shah et al's MOG [318] and Chen et Ellis'MOG [72] both published in 2014 achieve significantly better detection while being usable in real applications than previously published MOG algorithms, that are MOG in 1999, Adaptive GMM P1C2-MOG-92 in 2003, Zivkovic-Heijden GMM [430] in 2004, and Effective GMM [204] in 2005. Furthermore, there also exist real-time implementation of MOG [276][220] [312][311] [346], codebook [345], ViBe [195][198] and PBAS [196][197][115]. In addition, robust background initialization methods [291][349] as well as robust deep learned features [316][317] with the MOG model could also be considered for very challenging environments like maritime and submarine environments.…”

Section: Solved and Unsolved Challengesmentioning

confidence: 99%

Background Subtraction in Real Applications: Challenges, Current Models and Future Directions

Bouwmans

García-García²

2019

Preprint

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Computer vision applications based on videos often require the detection of moving objects in their first step. Background subtraction is then applied in order to separate the background and the foreground. In literature, background subtraction is surely among the most investigated field in computer vision providing a big amount of publications. Most of them concern the application of mathematical and machine learning models to be more robust to the challenges met in videos. However, the ultimate goal is that the background subtraction methods developed in research could be employed in real applications like traffic surveillance. But looking at the literature, we can remark that there is often a gap between the current methods used in real applications and the current methods in fundamental research. In addition, the videos evaluated in large-scale datasets are not exhaustive in the way that they only covered a part of the complete spectrum of the challenges met in real applications. In this context, we attempt to provide the most exhaustive survey as possible on real applications that used background subtraction in order to identify the real challenges met in practice, the current used background models and to provide future directions. Thus, challenges are investigated in terms of camera, foreground objects and environments. In addition, we identify the background models that are effectively used in these applications in order to find potential usable recent background models in terms of robustness, time and memory requirements.

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“…The difference between the absolute value of the frames has to be greater than the threshold. According to [12], the algorithm is placed in multiple categories, from history-based algorithm to adaptive learning algorithm. We chose adaptive learning algorithm over historybased algorithm [13].…”

Section: Mixture Of Gaussianmentioning

confidence: 99%