Pattern-based video coding with dynamic background modeling

Paul, Manoranjan; Wang, Lin; Lau, Chiew Tong; Lee, Bu-Sung

doi:10.1186/1687-6180-2013-138

Cited by 12 publications

(4 citation statements)

References 32 publications

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“…Thus, the proposed model focuses on rate-distortion optimization whereas the original MOG primarily focuses on moving object detection. In a further work, Paul et al [271] presented an arbitrary shaped pattern-based video coding (ASPVC) for dynamic background modeling based on MD-MOG. Even if these dynamic background frame based video coding methods based on MoG based background modeling achieve better rate distortion performance compared to the H.264 standard, they need high computation time, present low coding efficiency for dynamic videos, and prior knowledge requirement of video content.…”

Section: Content-based Video Codingmentioning

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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Section: Content-based Video Codingmentioning

confidence: 99%

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

Bouwmans

García-García²

2019

Preprint

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“…It also established the need for using a good DBM technique for practical usage. A number of studies have highlighted the improved performance and efficiency of the DBM based McFIS scheme [5] [6] [19]- [21]. DBM is also applied in recent studies in the area of transcoding technique for video surveillance [22].…”

Section: Introductionmentioning

confidence: 99%

“…MoG based DBM works at the pixel level where each pixel of a scene is modeled independently using a mixture of Gaussian distributions (generally 3 to 5) [21]- [23][25] [26]. Although the MoG based DBM has proved successful and is widely used by researchers and practitioners, it requires the user to assume the data distribution in advance and relevant parameters must be set based on this underlying assumption.…”

Section: Introductionmentioning

confidence: 99%

Adaptive weighted non-parametric background model for efficient video coding

et al. 2017

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“…In recent studies dynamic backgrounds are used in video coding techniques [4], [7]- [8]. Paul et al [4], [7] utilised the concept of the most common frame in a scene (McFIS) applying the Gaussian mixture [9]- [11] based dynamic background modelling for video coding.…”

Section: Introductionmentioning

confidence: 99%

Joint texture and depth coding using cuboid data compression

Paul

Chakraborty

Murshed

et al. 2015

2015 18th International Conference on Computer and Information Technology (ICCIT)

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The latest multiview video coding (MVC) standards such as 3D-HEVC and H.264/MVC normally encodes texture and depth videos separately. Significant amount of ratedistortion performance and computational performance are sacrificed due to separate encoding due to the lack of exploitation of joint information. Obviously, separate encoding also creates synchronization issue for 3D scene formation in the decoder. Moreover, the hierarchical frame referencing architecture in the MVC creates random access frame delay. In this paper we develop an encoder and decoder framework where we can encode texture and depth video jointly by forming and encoding 3D cuboid using high dimensional entropy coding. The results from our experiments show that our proposed framework outperforms the 3D-HEVC in rate-distortion performance and reduces the computational time significantly by reducing random access frame delay. Keywords-video coding; cuboid; McFIS; dynamic background; mutiview video I.

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Pattern-based video coding with dynamic background modeling

Cited by 12 publications

References 32 publications

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

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

Adaptive weighted non-parametric background model for efficient video coding

Joint texture and depth coding using cuboid data compression

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