WeSamBE: A Weight-Sample-Based Method for Background Subtraction

Jiang, Shujuan; Lu, Xiaobo

doi:10.1109/tcsvt.2017.2711659

Cited by 142 publications

(97 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed algorithm showed lower performance than PAWCS [10], but better performance than other algorithms. The proposed algorithm showed lower performance than PAWCS [10] and WeSamBE [15] in terms of precision and FPR. However, the proposed method showed better performance than these algorithms in terms of FNR.…”

Section: 1) Evaluation For Cdnet Datasetmentioning

confidence: 84%

“…Feedback Process is a module that calculates ( ) and t ( ) parameters used in Background samples and BG/FG classification module. Equations (14) and (15) are formulas used to calculate parameters. The updating method of ( ) and t ( ) is the same as that of SuBSENSE [7].…”

Section: 4) Feedback Processmentioning

confidence: 99%

“…Table 4 compares the processing speed of the background subtraction method with the proposed algorithm in the above environment. WeSamBE [15] 0.5~1 Table 5 shows comparison results between the proposed algorithm and other algorithms. The proposed method had a processing speed of 11 ~ 12 fps when only CPU was used.…”

Section: 1) Evaluation For Cdnet Datasetmentioning

confidence: 99%

See 2 more Smart Citations

WisenetMD: Motion Detection Using Dynamic Background Region Analysis

Lee

Yoo

et al. 2019

Symmetry

View full text Add to dashboard Cite

Motion detection algorithms that can be applied to surveillance cameras such as CCTV (Closed Circuit Television) have been studied extensively. Motion detection algorithm is mostly based on background subtraction. One main issue in this technique is that false positives of dynamic backgrounds such as wind shaking trees and flowing rivers might occur. In this paper, we proposed a method to search for dynamic background region by analyzing the video and removing false positives by re-checking false positives. The proposed method was evaluated based on CDnet 2012/2014 dataset obtained at "changedetection.net" site. We also compared its processing speed with other algorithms.

show abstract

Section: 1) Evaluation For Cdnet Datasetmentioning

confidence: 84%

Section: 4) Feedback Processmentioning

confidence: 99%

See 1 more Smart Citation

WisenetMD: Motion Detection Using Dynamic Background Region Analysis

Lee

Yoo

et al. 2019

Symmetry

View full text Add to dashboard Cite

show abstract

“…While computation time is a critical factor for justifying background estimation, most of the literature focuses on incrementally improving accuracy with new algorithms at any cost. Popular pixel-level models such as the Gaussian Mixture Model (GMM) [1] have been around for decades, but recent approaches have included applying adaptive weights and parameters [2,3], deep convolutional neural networks [4][5][6], and ensemble models with stochastic model optimisation [7], all of which significantly increase computation time while only marginally improving accuracy, failing to address the challenges of real-world implementation.…”

Section: Introductionmentioning

confidence: 99%

Accelerating SuperBE with Hardware/Software Co-Design

et al. 2018

View full text Add to dashboard Cite

Background Estimation is a common computer vision task, used for segmenting moving objects in video streams. This can be useful as a pre-processing step, isolating regions of interest for more complicated algorithms performing detection, recognition, and identification tasks, in order to reduce overall computation time. This is especially important in the context of embedded systems like smart cameras, which may need to process images with constrained computational resources. This work focuses on accelerating SuperBE, a superpixel-based background estimation algorithm that was designed for simplicity and reducing computational complexity while maintaining state-of-the-art levels of accuracy. We explore both software and hardware acceleration opportunities, converting the original algorithm into a greyscale, integer-only version, and using Hardware/Software Co-design to develop hardware acceleration components on FPGA fabric that assist a software processor. We achieved a 4.4× speed improvement with the software optimisations alone, and a 2× speed improvement with the hardware optimisations alone. When combined, these led to a 9× speed improvement on a Cyclone V System-on-Chip, delivering almost 38 fps on 320 × 240 resolution images.

show abstract

“…In addition, a recent history model (RHM) is generated by keeping the last five-pixel intensities as samples. The RHM is computed in Eq (5)…”

mentioning

confidence: 99%

CANDID: Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction

Mandal

Saxena

Vipparthi

et al. 2018

2018 24th International Conference on Pattern Recognition (ICPR)

View full text Add to dashboard Cite

Background subtraction in video provides the preliminary information which is essential for many computer vision applications. In this paper, we propose a sequence of approaches named CANDID to handle the change detection problem in challenging video scenarios. The CANDID adaptively initializes the pixel-level distance threshold and update rate. These parameters are updated by computing the change dynamics at a location. Further, the background model is maintained by formulating a deterministic update policy. The performance of the proposed method is evaluated over various challenging scenarios such as dynamic background and extreme weather conditions. The qualitative and quantitative measures of the proposed method outperform the existing state-of-the-art approaches.

show abstract

WeSamBE: A Weight-Sample-Based Method for Background Subtraction

Cited by 142 publications

References 38 publications

WisenetMD: Motion Detection Using Dynamic Background Region Analysis

WisenetMD: Motion Detection Using Dynamic Background Region Analysis

Accelerating SuperBE with Hardware/Software Co-Design

CANDID: Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction

Contact Info

Product

Resources

About