Background-foreground information based bit allocation algorithm for surveillance video on high efficiency video coding (HEVC)

Li, Xiujuan; Abudoulikemu, Yimamu'aishan

doi:10.1109/vcip.2016.7805562

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…As a widely used approach in various computer vision and video processing applications [1], [2], scene background initialization plays an active role in object detection [3], video segmentation [4], video coding [5], [6] and video inpainting [7], [8], etc. Scene background initialization describes the scene without any foreground objects and generates a clear background to facilitate more efficient follow-up processing in computer vision or video processing applications.…”

Section: Introductionmentioning

confidence: 99%

Co-occurrence Background Model with Superpixels for Robust Background Initialization

Zhou,

Deng,

Peng

et al. 2020

Preprint

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Background initialization is an important step in many high-level applications of video processing, ranging from video surveillance to video inpainting. However, this process is often affected by practical challenges such as illumination changes, background motion, camera jitter and intermittent movement, etc. In this paper, we develop a co-occurrence background model with superpixel segmentation for robust background initialization. We first introduce a novel co-occurrence background modeling method called as Co-occurrence Pixel-Block Pairs (CPB) to generate a reliable initial background model, and the superpixel segmentation is utilized to further acquire the spatial texture information of foreground and background. Then, the initial background can be determined by combining the foreground extraction results with the superpixel segmentation information. Experimental results obtained from the dataset of the challenging benchmark (SBMnet) validate it's performance under various challenges.

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Section: Introductionmentioning

confidence: 99%

Co-occurrence Background Model with Superpixels for Robust Background Initialization

Zhou,

Deng,

Peng

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Scene background initialization refers to approaches automatically producing a stationary scene without foreground objects, given a set of training frames. It is a critical step in many computer vision and video processing applications such as object detection and tracking [1,2], video segmentation [3], video coding [4,5,6], video inpainting [7] and so on. In such applications, obtaining the background image is generally a pre-processing step and the clear background can make the processing more effective.…”

Section: Introductionmentioning

confidence: 99%

A robust background initialization algorithm with superpixel motion detection

Min

Cheung

2019

Signal Processing: Image Communication

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Scene background initialization allows the recovery of a clear image without foreground objects from a video sequence, which is generally the first step in many computer vision and video processing applications. The process may be strongly affected by some challenges such as illumination changes, foreground cluttering, intermittent movement, etc. In this paper, a robust background initialization approach based on superpixel motion detection is proposed. Both spatial and temporal characteristics of frames are adopted to effectively eliminate foreground objects. A subsequence with stable illumination condition is first selected for background estimation. Images are segmented into superpixels to preserve spatial texture information and foreground objects are eliminated by superpixel motion filtering process. A low-complexity densitybased clustering is then performed to generate reliable background candidates for final background determination. The approach has been evaluated on SBMnet dataset and it achieves a performance superior or comparable to other state-of-the-art works with faster processing speed. Moreover, in those complex and dynamic categories, the algorithm produces the best results showing the robustness against very challenging scenarios.

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“…Chen et al [4] study the RDO optimization of the background reference CTUs without exploiting the RD characteristics of the foreground CTUs; in their work, the foreground CTU is extracted from Manuscript the trained background frame, which inevitably brings coding complexity. Li et al [5] study the bit allocation scheme for the foreground and background separately, but the most important parameter (λ) is not considered carefully. The authors of [6] calculate the λ values for ROI and non-ROI with the independent bit rate allocation.…”

Section: Introductionmentioning

confidence: 99%

A Foreground-Background-Based CTU λ Decision Algorithm for HEVC Rate Control of Surveillance Videos

Yang

Guo-zhong

Teng

2019

IEICE Trans. Inf. & Syst.

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Although HEVC rate control can achieve high coding efficiency, it still does not fully utilize the special characteristics of surveillance videos, which typically have a moving foreground and relatively static background. For surveillance videos, it is usually necessary to provide a better coding quality of the moving foreground. In this paper, a foregroundbackground CTU λ separate decision scheme is proposed. First, lowcomplexity pixel-based segmentation is presented to obtain the foreground and the background. Second, the rate distortion (RD) characteristics of the foreground and the background are explored. With the rate distortion optimization (RDO) process, the average CTU λ value of the foreground or the background should be equal to the frame λ. Then, a separate optimal CTU λ decision is proposed with a separate λ clipping method. Finally, a separate updating process is used to obtain reasonable parameters for the foreground and the background. The experimental results show that the quality of the foreground is improved by 0.30 dB in the random access configuration and 0.45 dB in the low delay configuration without degradation of either the rate control accuracy or whole frame quality.

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Background-foreground information based bit allocation algorithm for surveillance video on high efficiency video coding (HEVC)

Cited by 5 publications

References 7 publications

Co-occurrence Background Model with Superpixels for Robust Background Initialization

Co-occurrence Background Model with Superpixels for Robust Background Initialization

A robust background initialization algorithm with superpixel motion detection

A Foreground-Background-Based CTU λ Decision Algorithm for HEVC Rate Control of Surveillance Videos

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