PID-based regulation of background dynamics for foreground segmentation

Tiefenbacher, Philipp; Hofmann, Martin; Merget, Daniel; Rigoll, Gerhard

doi:10.1109/icip.2014.7025664

Cited by 11 publications

(6 citation statements)

References 15 publications

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“…They showed that it can cope with a dynamic background and highly structured scenes. Tiefenbacher et al [21] proposed an algorithm that improves the algorithm introduced by Hofmann et al [20] by controlling the updates of the pixel-wise thresholds using a PID controller. St-Charles et al [2] also proposed an improved algorithm by using local binary similarity patterns [23] as additional features of pixel intensities and slight modification of the update mechanism of the thresholds and the background model.…”

Section: A Earlier Approaches: Non Deep Learning Basedmentioning

confidence: 99%

Foreground Objects Detection Using a Fully Convolutional Network With a Background Model Image and Multiple Original Images

Kim

2020

IEEE Access

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Visual surveillance aims to reliably extract foreground objects. Traditional algorithms usually use a background model image which is generated through the probabilistic modeling of changes over time and space. They detect foreground objects by comparing a background model image with a current image. Hard shadows, illumination changes, camouflage, camera jitter, and ghost object motion make the robust detection of foreground objects difficult in visual surveillance. Recently, various methods based on deep learning have been applied to visual surveillance. It has been shown that deep learning approaches can stably extract salient features, and they give a superior result compared to traditional algorithms. However, they show a good performance only for scenes that are similar to a scene used in training. Without retraining on a new scene, they give a worse result compared to traditional algorithms. In this paper, we propose a stable foreground object detection algorithm through the integration of a background model image used in traditional methods and deep learning methods. A background model image generated by SuBSENSE and multiple images are used as the input of a fully convolutional network. Also, it is shown that it is possible to improve a generalization power by training the proposed network using diverse scenes from an open dataset. We show that the proposed algorithm can have a superior result compared to deep learning-based and traditional algorithms in a new scene without retraining the network. The performance of the proposed algorithm is evaluated using various datasets such as the CDnet 2014, SBI, LASIESTA, and our own datasets. The proposed algorithm shows improvement of 17.5%, 8.9%, and 4.3%, respectively, in FM score compared to three deep learning-based algorithms. INDEX TERMS Foreground objects detection, visual surveillance, generalization power, deep learning, fully convolutional network.

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Section: A Earlier Approaches: Non Deep Learning Basedmentioning

confidence: 99%

Foreground Objects Detection Using a Fully Convolutional Network With a Background Model Image and Multiple Original Images

Kim

2020

IEEE Access

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“…SACON [22] ViBe [23] ViBe + [24] LOBSTER [2] PBAS [5] PID [25] SVDBP [4] SuBSENSE [3] Proposed FBGS MOG [37] KNN [17] GMM [14] FTSG [19] SBS [20] SC-SOBS [38] WNN [33] CNN [34] DeepBS [26] FIGURE 2 Taxonomy of background subtraction approach up objects, and so on. The method can only detect the boundary edge of evenly coloured intensity or grey intensity objects.…”

Section: Sample Consensus-based Methodsmentioning

confidence: 99%

“…Also, the sample consensus‐based approaches [2, 22, 23] achieved some success with intensity changes, but the methods sharply decreased in accuracy when there are large, unexpected changes in a video. To work in dynamic backgrounds (either mild or large changes in background), sample consensus‐based approaches [2–5, 24, 25] added some pixel descriptors to create an illumination‐ and shadow‐invariant model, and introduced a dynamic background control mechanism. Later on, deep learning‐based BGS (DeepBS) [26] exploited two methods [3, 19] to create backgrounds to train and test the model.…”

Section: Introductionmentioning

confidence: 99%

The trade‐off between accuracy and the complexity of real‐time background subtraction

Hossain

Nguyen

Huh

2020

IET Image Processing

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Background subtraction used in object detection, tracking and action recognition is a typical method that separates foreground objects from the background. These applications require accuracy and a complexity reduction technique. Some approaches have been proposed to either increase accuracy or decrease complexity. However, the trade‐off between increasing accuracy and reducing the complexity of background subtraction is a big challenge. To address this issue, a background subtraction‐based real‐time moving object‐detection approach is proposed. The key contribution in authors' proposal is to use a colour image and a novel colour‐gradient blending fused image to achieve accurate background/foreground segmentation. The fused image is a combination of a gradient image and a colour image to correct illumination variations and preserve the edge information. Also, thresholds are adaptively selected based on the dynamic background behaviour to attain a more robust classification system. The proposed model based on real‐time and complex videos from the CD‐2012 and CD‐2014 change detection data sets, and the CMD data set is evaluated. Experimental results indicate that authors' method processes around 43 frames per second and requires six bytes of memory per pixel, which is noticeably more efficient and less complex than other background subtraction methods.

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“…In an other work, Rout et al [307] designed a spatio-Contextual GMM (SC-GMM) that outperforms 18 background subtraction algorithms such as 1) classical algorithms like the original MOG, the original KDE and the original PCA, and 2) advanced algorithms like DPGMM (VarDMM) [132], PBAS [142], PBAS-PID [356], SuBSENSE [340] and SOBS [233] both on the Fish4Knowledge dataset [180] and the dataset UnderwaterChangeDetection.eu [293].…”

Section: Open Sea Environmentsmentioning

confidence: 99%

Background Subtraction in Real Applications: Challenges, Current Models and Future Directions

Bouwmans

García-García²

2019

Preprint

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Computer vision applications based on videos often require the detection of moving objects in their first step. Background subtraction is then applied in order to separate the background and the foreground. In literature, background subtraction is surely among the most investigated field in computer vision providing a big amount of publications. Most of them concern the application of mathematical and machine learning models to be more robust to the challenges met in videos. However, the ultimate goal is that the background subtraction methods developed in research could be employed in real applications like traffic surveillance. But looking at the literature, we can remark that there is often a gap between the current methods used in real applications and the current methods in fundamental research. In addition, the videos evaluated in large-scale datasets are not exhaustive in the way that they only covered a part of the complete spectrum of the challenges met in real applications. In this context, we attempt to provide the most exhaustive survey as possible on real applications that used background subtraction in order to identify the real challenges met in practice, the current used background models and to provide future directions. Thus, challenges are investigated in terms of camera, foreground objects and environments. In addition, we identify the background models that are effectively used in these applications in order to find potential usable recent background models in terms of robustness, time and memory requirements.

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PID-based regulation of background dynamics for foreground segmentation

Cited by 11 publications

References 15 publications

Foreground Objects Detection Using a Fully Convolutional Network With a Background Model Image and Multiple Original Images

Foreground Objects Detection Using a Fully Convolutional Network With a Background Model Image and Multiple Original Images

The trade‐off between accuracy and the complexity of real‐time background subtraction

Background Subtraction in Real Applications: Challenges, Current Models and Future Directions

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