A robust background initialization algorithm with superpixel motion detection

Xu, Zhe; Min, Biao; Cheung, Ray C. C.

doi:10.1016/j.image.2018.07.004

Cited by 20 publications

(20 citation statements)

References 36 publications

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“…then, we can acquire an accuracy result of background initialization. The proposed approach is compared with seven different state-of-the-art techniques:LaBGen-OF [6], MSCL [16], FSBE [17], LaBGen-P-Semantic(MP+U) [8], SPMD [18], FC-FlowNet [11] and BEWiS [9] selected from SBMnet benchmark. Four of them are the leading techniques for background initialization in SBMnet benchmark, especially MSCL [16] which is the top ranked techniques at present and two of them [9,11] are the neural network (NN) based methods.…”

Section: Background Initializationmentioning

confidence: 99%

Robust Spatial-Temporal Correlation Model for Background Initialization in Severe Scene

Deng

Zhou

Peng

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Scene background initialization is an important step as one low-layer method for high-layer applications in computer vision. However, this process is always affected by practical challenges such as illumination changes, background motion, camera jitter, intermittent movement and bad weather outdoors, etc. In this work, we develop a novel method called co-occurrence pixel-block (CPB) model via spatial-temporal correlation for robust background initialization. This work first introduces the CPB method for foreground extraction. And then, background information in spatial-temporal features are utilized to recover an adaptive background for the current frame. Experimental results obtained from the dataset of the challenging benchmark (SBMnet) validate it's performance under various challenges.

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Section: Background Initializationmentioning

confidence: 99%

Robust Spatial-Temporal Correlation Model for Background Initialization in Severe Scene

Deng

Zhou

Peng

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…In order to evaluate the performance of each model more accurately and objectively in the quantitative comparative analysis, we refer to [48] and [49]…”

Section: Quantitative Analysis Indicatorsmentioning

confidence: 99%

Video Background/Foreground Separation Model Based on Non-Convex Rank Approximation RPCA and Superpixel Motion Detection

et al. 2020

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Traditional robust principal component analysis (RPCA) is very prone to voids in the process of background/foreground separation of complex scene videos and easy to misjudge the dynamic background as a moving target, which makes the separation effect unideal. In order to address this problem, this paper introduces the super-pixel segmentation technique into the RPCA model. First, the Linear Spectral Clustering algorithm (LSC) is used to mark the super-pixel segmentation of the video sequence and a superpixel grouping matrix is obtained. Then a new video background/foreground separation model is proposed based on the non-convex rank approximation RPCA and super-pixel motion detection (SPMD) technique. The Otsu algorithm is used to obtain the motion mask matrix and the augmented lagrange alternating direction method is used to solve the improved RPCA model. The results of numerical experiment show that the method proposed in this paper has a higher accuracy in the detection of moving objects in dynamic background. INDEX TERMS Video background/foreground separation, RPCA, superpixel segmentation, linear spectral clustering algorithm, Otsu algorithm, motion mask.

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“…16 for series of video frames from SBMnet dataset. Application examples of this dataset for background initialization and background modeling are [145]- [149].…”

Section: B Sbm Net Datasetmentioning

confidence: 99%

A Comprehensive Survey of Video Datasets for Background Subtraction

Kalsotra

Arora

2019

IEEE Access

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Background subtraction is an effective method of choice when it comes to detection of moving objects in videos and has been recognized as a breakthrough for the wide range of applications of intelligent video analytics (IVA). In recent years, a number of video datasets intended for background subtraction have been created to address the problem of large realistic datasets with accurate ground truth. The use of these datasets enables qualitative as well as quantitative comparisons and allows benchmarking of different algorithms. Finding the appropriate dataset is generally a cumbersome task for an exhaustive evaluation of algorithms. Therefore, we systematically survey standard video datasets and list their applicability for different applications. This paper presents a comprehensive account of public video datasets for background subtraction and attempts to cover the lack of a detailed description of each dataset. The video datasets are presented in chronological order of their appearance. Current trends of deep learning in background subtraction along with top-ranked background subtraction methods are also discussed in this paper. The survey introduced in this paper will assist researchers of the computer vision community in the selection of appropriate video dataset to evaluate their algorithms on the basis of challenging scenarios that exist in both indoor and outdoor environments.INDEX TERMS Background model, background subtraction, challenges, datasets, deep neural networks, foreground, intelligent video analytics (IVA), video frames.

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A robust background initialization algorithm with superpixel motion detection

Cited by 20 publications

References 36 publications

Robust Spatial-Temporal Correlation Model for Background Initialization in Severe Scene

Robust Spatial-Temporal Correlation Model for Background Initialization in Severe Scene

Video Background/Foreground Separation Model Based on Non-Convex Rank Approximation RPCA and Superpixel Motion Detection

A Comprehensive Survey of Video Datasets for Background Subtraction

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