Moving Object Detection Based on Non-Convex RPCA With Segmentation Constraint

Hu, Zixuan; Wang, Yongli; Su, Rui; Bian, Xinxin; Wei, Hongchao; He, Ge

doi:10.1109/access.2020.2977273

Cited by 17 publications

(8 citation statements)

References 48 publications

(67 reference statements)

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“…Table 2 displays the comparison analysis of object detection with different existing methods by means of different categories in the CDNet 2014 dataset. The performance of proposed methodology is compared with existing methods like robust principal component‐principal component analysis (RPCA‐PCA), nonconvex‐RPCA (Noncvx‐RPCA), weighted nuclear norm minimization‐RPCA (WNNM‐RPCA), low rank and sparse‐matrix decomposition via the truncated nuclear norm and sparse regularizer (LRSD‐TNNSR), and non‐convex rank approximation function‐RPCA (NCSC‐RPCA) 27 . When compared with the above‐mentioned existing methodologies, our proposed design outperforms all existing methods in terms of different performance parameters like recall, precision, and F‐measure.…”

Section: Resultsmentioning

confidence: 99%

An accurate foreground moving object detection based on segmentation techniques and optimal classifier

Nagaraju¹,

Babu

Somayajulu³

et al. 2021

Concurrency and Computation

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In video surveillance schemes, the motion object detection plays a significant role. To subtract the object background, a segmentation technique based on feature extraction is utilized in which the change in the training rate makes an alteration in the background. Thereafter, the extracted features are trained by using the self‐organizing map (SOM) network in which the weight parameters in the network is optimized with the help of artificial bee colony (ABC) optimization algorithm, so, the proposed methodology is named as HSOM‐ABC technique. This methodology is carried out to perform the classification process in this research. Initially, the whole dataset is preprocessed with the help of grayscale conversion method which converts the original image into grayscale color. After this, fuzzy c‐means clustering is applied to perform the segmentation process and this method divides the foreground and background parts efficiently. Then, feature extraction is done with the help of local binary pattern method which extract the relevant features from the segmented image. Finally, HSOM‐ABC method is proposed to accurate classification process. Hence, the moving objects are identified by categorizing the background and foreground images. MatLab platform is chosen for the proposed work simulation and the performance is evaluated by means of different parameters and it is compared with new existing approaches. Experimental outcomes show that the proposed strategy achieves higher precision value than any other existing methods.

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Section: Resultsmentioning

confidence: 99%

An accurate foreground moving object detection based on segmentation techniques and optimal classifier

Nagaraju¹,

Babu

Somayajulu³

et al. 2021

Concurrency and Computation

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show abstract

“…In this section, we will verify the performance of the model proposed in this paper through experiments, using a combination of quantitative and qualitative methods. The algorithm in this paper was compared with GoDec [13], NC-RPCA [14], AccAltProj [44], KNN [45], LRSD-TNNSR [46] and NCSC-RPCA [47]. We mainly selected eight video…”

Section: Experimental Results and Analysismentioning

confidence: 99%

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

et al. 2020

Self Cite

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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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“…Then, in [50], a super-pixel based tree structure is arranged for the foreground objects detection. In 2020, super-pixel segmentation technology is allocated for the foreground objects while the background is modelled by a non-convex approximation [51].…”

Section: Spatiotemporal Solutionsmentioning

confidence: 99%

A Tensor Foreground-Background Separation Algorithm Based on Dynamic Dictionary Update and Active Contour Detection

Pei

Yang

et al. 2020

IEEE Access

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Foreground-background separation of surveillance video, that models static background and extracts moving foreground simultaneously, attracts increasing attentions in building a smart city. Conventional techniques towards this always consider the background as primary target and tend to adopt low-rank constraint as its estimator, which provides finite (equal to the value of rank) alternatives when constructing the background. However, in practical missions, although general sketch of background is stable, some details change constantly. Aimed at this, we propose to represent the general background by a linear combination of some atoms and record the detailed background by spatiotemporal clustered patches. Then, the moving foreground is considered as a mixture of active contours and continuous contents. Eventually, joint optimization is conducted under a unified framework, i.e., alternating direction multipliers method (ADMM), and produces our tensor model for hierarchical background and hierarchical foreground separation (THHS). The employed tensor space, which agrees with the instinct structure of video data, benefits all the spatiotemporal designs in both background modular and foreground part. Experimental results show that THHS is more adaptive to the dynamic background and produces more accurate foreground when compared against current state-of-the-art techniques.INDEX TERMS Dictionary learning, foreground-background separation, active contour model, quantization, hierarchical modeling.

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Moving Object Detection Based on Non-Convex RPCA With Segmentation Constraint

Cited by 17 publications

References 48 publications

An accurate foreground moving object detection based on segmentation techniques and optimal classifier

An accurate foreground moving object detection based on segmentation techniques and optimal classifier

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

A Tensor Foreground-Background Separation Algorithm Based on Dynamic Dictionary Update and Active Contour Detection

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