Foreground Segmentation in Videos Combining General Gaussian Mixture Modeling and Spatial Information

Boulmerka, Aissa; Állili, Madjid

doi:10.1109/tcsvt.2017.2665970

Cited by 25 publications

(8 citation statements)

References 60 publications

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“…In our experiments, we compare our detector with other state-of-the-art methods, which are FCB [40], MoGG [41], and SGF [42], respectively. To conduct a quantitative comparison between the proposed method and other approaches, we use the evaluation metrics provided by the CDnet dataset, which are Recall (R), Precision (P) and Fmeasure (F_M).…”

Section: A Detectionmentioning

confidence: 99%

Multiple Object Tracking Using Edge Multi-Channel Gradient Model With ORB Feature

Chen

Chi

et al. 2021

IEEE Access

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Section: A Detectionmentioning

confidence: 99%

Multiple Object Tracking Using Edge Multi-Channel Gradient Model With ORB Feature

Chen

Chi

et al. 2021

IEEE Access

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“…Siva et al [17] extend the work of Zivkovic and Heijden [18] and combine a GMM with a conditional probabilistic function which attempts to model the pixel intensity values affected by sudden local illumination change. Boulmerka and Allili [19] combine a GMM with inter-frame correlation analysis and histogram matching. Akilan et al [20] enhance the results of a GMM model by fusing features of color similarity, color distortion, and illumination measures.…”

Section: Related Work a Gaussian Mixture Modelsmentioning

confidence: 99%

Illumination-Aware Multi-Task GANs for Foreground Segmentation

Sakkos

Shum

2019

IEEE Access

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Foreground-background segmentation has been an active research area over the years. However, conventional models fail to produce accurate results when challenged with the videos of challenging illumination conditions. In this paper, we present a robust model that allows accurately extracting the foreground even in exceptionally dark or bright scenes and in continuously varying illumination in a video sequence. This is accomplished by a triple multi-task generative adversarial network (TMT-GAN) that effectively models the semantic relationship between the dark and bright images and performs binary segmentation end-to-end. Our contribution is twofold: first, we show that by jointly optimizing the GAN loss and the segmentation loss, our network simultaneously learns both tasks that mutually benefit each other. Second, fusing features of images with varying illumination into the segmentation branch vastly improve the performance of the network. Comparative evaluations on highly challenging real and synthetic benchmark datasets (ESI and SABS) demonstrate the robustness of TMT-GAN and its superiority over state-of-the-art approaches. INDEX TERMS Background subtraction, multi-task learning, generative adversarial networks, video segmentation, illumination-aware.

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“…The ROIs (in this study) are the foreground (FG) consisting of the nonstationary objects and the background (BG) containing the immovable objects in the video. Static camera devices are always fixed to a position, making it easy to detect the FG information in the form of motion by utilizing background subtraction [11]- [13] technique. However, dynamic camera devices resulting in a complex situation, as the BG changes along with the FG information.…”

Section: Introductionmentioning

confidence: 99%

SecureCam: Selective Detection and Encryption Enabled Application for Dynamic Camera Surveillance Videos

Aribilola

Asghar

Kanwal

et al. 2023

IEEE Trans. Consumer Electron.

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Foreground Segmentation in Videos Combining General Gaussian Mixture Modeling and Spatial Information

Cited by 25 publications

References 60 publications

Multiple Object Tracking Using Edge Multi-Channel Gradient Model With ORB Feature

Multiple Object Tracking Using Edge Multi-Channel Gradient Model With ORB Feature

Illumination-Aware Multi-Task GANs for Foreground Segmentation

SecureCam: Selective Detection and Encryption Enabled Application for Dynamic Camera Surveillance Videos

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