ASIC and FPGA Implementation of the Gaussian Mixture Model Algorithm for Real-Time Segmentation of High Definition Video

Genovese, Mariangela; Napoli, Ettore

doi:10.1109/tvlsi.2013.2249295

Cited by 77 publications

(57 citation statements)

References 19 publications

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“…In the past years, some work has been reported on hardware implementation/acceleration of MoG algorithm for video segmentation [7][8][9][10][11][12][13][14]. Early work on hardware implementation of MoG algorithm was conducted in [8,9].…”

Section: Introduction and Previous Workmentioning

confidence: 99%

“…The design in [7] can meet the real-time requirement of most high-frame-rate high-resolution segmentation applications (such as VGA resolution of 640 × 480 at 25 fps in real-time). [10,11] reported work to further improve the performance of MoG hardware implementation to be able to support full high-definition (1080 × 1920) video segmentation in real-time. [10] targets a reduction in the power consumption of an FPGA-based hardware implementation, which was claimed to consume 600 times less power compared to the traditional embedded software implementation.…”

Section: Introduction and Previous Workmentioning

confidence: 99%

“…[10] targets a reduction in the power consumption of an FPGA-based hardware implementation, which was claimed to consume 600 times less power compared to the traditional embedded software implementation. [11] presents a customized implementation of the MoG algorithm for full high-definition videos to reduce power consumption and hardware resources after simplifying/modifying the MoG algorithm. To further increase the performance of MoG hardware implementation, recent work in [12][13][14] explored to leverage the power of modern multi-core processors and graphic-processing-units (GPU) with parallel or vector processing techniques.…”

Section: Introduction and Previous Workmentioning

confidence: 99%

See 2 more Smart Citations

SoC Hardware Implementation of Real-Time Video Segmentation based on the Mixture of Gaussian Algorithm

Tang

2017

JLPEA

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Abstract:Video segmentation based on the Mixture of Gaussian (MoG) algorithm is widely used in video processing systems, and hardware implementations have been proposed in the past years. Most previous work focused on high-performance custom design of the MoG algorithm to meet real-time requirement of high-frame-rate high-resolution video segmentation tasks. This paper focuses on the System-on-Chip (SoC) design and the priority is SoC integration of the system for flexibility/adaptability, while at the same time, custom design of the original MoG algorithm is included. To maximally retain the accuracy of the MoG algorithm for best segmentation performance, we minimally modified the MoG algorithm for hardware implementation at the cost of hardware resources. The MoG algorithm is custom-implemented as a hardware IP (Intellectual Property), which is then integrated within an SoC platform together with other video processing components, so that some key control parameters can be configured on-line, which makes the video segmentation system most suitable for different scenarios. The proposed implementation has been demonstrated and tested on a Xilinx Spartan-3A DSP Video Starter Board. Experiment results show that under a clock frequency of 25 MHz, this design meets the real-time requirement for VGA resolution (640 × 480) at 30 fps (frame-per-second).

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Section: Introduction and Previous Workmentioning

confidence: 99%

Section: Introduction and Previous Workmentioning

confidence: 99%

Section: Introduction and Previous Workmentioning

confidence: 99%

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SoC Hardware Implementation of Real-Time Video Segmentation based on the Mixture of Gaussian Algorithm

Tang

2017

JLPEA

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“…Background modeling [1], is often used in different applications to model the background and then detect the moving objects in the scene like in video surveillance [2], [ 3], optical motion capture [4]- [6] and multimedia [7]- [10]. The simplest way to model the background is to acquire a background image which doesn't include any moving object.…”

Section: Introductionmentioning

confidence: 99%

High Definition Video Segmentation Techniques-A Review

Sabeenian¹,

Lavanya²

2013

IJCEE

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Abstract-Real time segmentation of moving regions in image sequences is a fundamental step in many vision systems including automated visual surveillance human-machine interface and very low bandwidth telecommunications. Background identification is a common feature in many video processing systems. One of the most important back ground identification algorithm is the Gaussian Mixture Model algorithm (GMM).On implementation of the Gaussian mixture model on FPGA results in Reduction of the processing capability of the overall system. Trainable Segmentation is adapted to improve the processing capability. After analyzing and evaluating the performance we conclude with several promising directions for future research. Indexterms-Video segmentation, back ground identification.

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“…It has many successful applications in various areas such as computer vision, digital signal processing, etc. For example, some recent researches use Gaussian mixture model for object tracking and segmentation [3] [4]. In this paper, we attempt to predict precipitation events using Gaussian mixture model (GMM).…”

mentioning

confidence: 99%

Predicting Precipitation Events Using Gaussian Mixture Model

Ling¹,

Zhu²

2017

JDAIP

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In this paper, a Gaussian mixture model (GMM) based classifier is described to tell whether precipitation events will happen on a certain day at a certain time from historical meteorological data. The classifier deals with a two-class classification problem where one class represents precipitation events and the other represents non-precipitation events. The concept of ambiguity is introduced to represent cases where weather conditions between the two classes like drizzles, intermittent or overcast are more likely to happen. Six groups of experiments are carried out to evaluate the performance of the classifier using different configurations based on the observation data released by Shanghai Baoshan weather station. Specifically, a typical classification performance of about 75% accuracy, 30% precision and 80% recall is achieved for prediction tasks with a time span of 12 hours.

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ASIC and FPGA Implementation of the Gaussian Mixture Model Algorithm for Real-Time Segmentation of High Definition Video

Cited by 77 publications

References 19 publications

SoC Hardware Implementation of Real-Time Video Segmentation based on the Mixture of Gaussian Algorithm

SoC Hardware Implementation of Real-Time Video Segmentation based on the Mixture of Gaussian Algorithm

High Definition Video Segmentation Techniques-A Review

Predicting Precipitation Events Using Gaussian Mixture Model

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