Universal Background Subtraction Using Word Consensus Models

St-Charles, Pierre-Luc; Bilodeau, Guillaume-Alexandre; Bergevin, Robert

doi:10.1109/tip.2016.2598691

Cited by 124 publications

(75 citation statements)

References 58 publications

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“…it is calculated using the formula . We compared our method to six other recent foreground segmentation methods, the adaptive background modeling(ABMFS) [78], the word consensus model(PAWCS) [77], the Gaussian mixture model (GMM) [6], the sample consensus background model (SACON) [17], ViBe [76], the pixel-based adaptive segmentation (PBAS) [79], the boosted Gaussian model (BMOG) , and multimode background subtraction method(UMBS). Gaussian Mixture Model is a pixel-based parametric method and BMOG are pixel and blob-based parametric method.…”

Section: P Recision =mentioning

confidence: 99%

Background Modelling using a Q-Tree Based Foreground Segmentation

Banu

Maheswari

2020

SCPE

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Background modelling is an empirical part in the procedure of foreground mining of idle and moving objects. The foreground object detection has become a challenging phenomenon due to intermittent objects, intensity variation, image artefact and dynamic background in the video analysis and video surveillance applications. In the video surveillances application, a large amount of data is getting processed by everyday basis. Thus it needs an efficient background modelling technique which could process those larger sets of data which promotes effective foreground detection. In this paper, we presented a renewed background modelling method for foreground segmentation. The main objective of the work is to perform the foreground extraction only in the intended region of interest using proposed Q-Tree algorithm. At most all the present techniques consider their updates to the pixels of the entire frame which may result in inefficient foreground detection with a quick update to slow moving objects; The proposed method contract these defect by extracting the foreground object by controlling the region of interest (the region only where the background subtraction is to be performed) and thereby reducing the false positive and false negative. The extensive experimental results and the evaluation parameters of the proposed approach with the state of art method were compared against the most recent background subtraction approaches. Moreover, we use challenge change detection dataset and the efficiency of our method is analyzed in different environmental conditions (indoor, outdoor) from the CDnet2014 dataset and additional real time videos. The experimental results were satisfactorily verified the strengths and weakness of proposed method against the existing state-of-the-art background modelling methods.

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Section: P Recision =mentioning

confidence: 99%

Background Modelling using a Q-Tree Based Foreground Segmentation

Banu

Maheswari

2020

SCPE

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“…• τ BG = 0: the threshold value in Equation (10). • τ F G = 225: the threshold value in Equation (11). • φ = 100: the time subsampling factor for controlling the background model adaptation speed.…”

Section: B Parameter Settingmentioning

confidence: 99%

Background Subtraction With Real-Time Semantic Segmentation

et al. 2019

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Accurate and fast foreground object extraction is very important for object tracking and recognition in video surveillance. Although many background subtraction (BGS) methods have been proposed in the recent past, it is still regarded as a tough problem due to the variety of challenging situations that occur in real-world scenarios. In this paper, we explore this problem from a new perspective and propose a novel background subtraction framework with real-time semantic segmentation (RTSS). Our proposed framework consists of two components, a traditional BGS segmenter B and a real-time semantic segmenter S. The BGS segmenter B aims to construct background models and segments foreground objects. The realtime semantic segmenter S is used to refine the foreground segmentation outputs as feedbacks for improving the model updating accuracy. B and S work in parallel on two threads. For each input frame It, the BGS segmenter B computes a preliminary foreground/background (FG/BG) mask Bt. At the same time, the real-time semantic segmenter S extracts the object-level semantics St. Then, some specific rules are applied on Bt and St to generate the final detection Dt. Finally, the refined FG/BG mask Dt is fed back to update the background model. Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that our proposed method achieves stateof-the-art performance among all unsupervised background subtraction methods while operating at real-time, and even performs better than some deep learning based supervised algorithms. In addition, our proposed framework is very flexible and has the potential for generalization.

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“…We name this extended algorithm as MSCL-FL for the detection of foreground objects. The proposed MSCL-FL algorithm has shown better performance compared to stateof-the-art methods including TVRPCA [8], 2P-RPCA [15], DP-GMM [17], GRASTA [18], TLSFSD [19], SRPCA [39], LSD [31], 3TD [35], MODSM [36], MLRBS [38], PAWCS [42], SuBSENSE [43], BMTDL [44], GFL [54], GOSUS [55], LR-FSO [56], RMAMR [57], BRTF [63], GoDec [64], and DECOLOR [65] on publicly available datasets, such as Change Detection (CDnet) 2014 [50], I2R [26], Background Models Challenge (BMC) [49], and Wallflower [48]. The remaining content of this paper is organized as follows.…”

Section: Ground Truth Imagesmentioning

confidence: 99%

“…During the past few years, many research studies have been carried out on background subtraction or foreground detection [8], [11], [17], [20], [25], [42], [65] as well as background initialization [2], [4], [13], [32], [57]. In background subtraction, the emphasis is to improve the accuracy of foreground detection.…”

Section: Related Workmentioning

confidence: 99%

Background–Foreground Modeling Based on Spatiotemporal Sparse Subspace Clustering

Javed

Mahmood

Bouwmans

et al. 2017

IEEE Trans. on Image Process.

123

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Background estimation and foreground segmentation are important steps in many high-level vision tasks. Many existing methods estimate background as a low-rank component and foreground as a sparse matrix without incorporating the structural information. Therefore, these algorithms exhibit degraded performance in the presence of dynamic backgrounds, photometric variations, jitter, shadows, and large occlusions. We observe that these backgrounds often span multiple manifolds. Therefore, constraints that ensure continuity on those manifolds will result in better background estimation. Hence, we propose to incorporate the spatial and temporal sparse subspace clustering into the robust principal component analysis (RPCA) framework. To that end, we compute a spatial and temporal graph for a given sequence using motion-aware correlation coefficient. The information captured by both graphs is utilized by estimating the proximity matrices using both the normalized Euclidean and geodesic distances. The low-rank component must be able to efficiently partition the spatiotemporal graphs using these Laplacian matrices. Embedded with the RPCA objective function, these Laplacian matrices constrain the background model to be spatially and temporally consistent, both on linear and nonlinear manifolds. The solution of the proposed objective function is computed by using the linearized alternating direction method with adaptive penalty optimization scheme. Experiments are performed on challenging sequences from five publicly available datasets and are compared with the 23 existing state-of-the-art methods. The results demonstrate excellent performance of the proposed algorithm for both the background estimation and foreground segmentation.

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Universal Background Subtraction Using Word Consensus Models

Cited by 124 publications

References 58 publications

Background Modelling using a Q-Tree Based Foreground Segmentation

Background Modelling using a Q-Tree Based Foreground Segmentation

Background Subtraction With Real-Time Semantic Segmentation

Background–Foreground Modeling Based on Spatiotemporal Sparse Subspace Clustering

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