Background Modeling via Incremental Maximum Margin Criterion

Marghes, Cristina; Bouwmans, Thierry

doi:10.1007/978-3-642-22819-3_40

Cited by 6 publications

(5 citation statements)

References 33 publications

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“…The PCA has been exploited for subspace learning to handle illumination changes in the video sequences. Earlier methods used the discriminative [112], [113] and mixed [114] subspace learning models. However, the regular subspace models suffer from high sensitivity to noise, outliers and missing data.…”

Section: Deep Learning Models For Change Detectionmentioning

confidence: 99%

An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs

Mandal¹,

Vipparthi²

2021

Preprint

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Visual change detection, aiming at segmentation of video frames into foreground and background regions, is one of the elementary tasks in computer vision and video analytics. The applications of change detection include anomaly detection, object tracking, traffic monitoring, human machine interaction, behavior analysis, action recognition, and visual surveillance. Some of the challenges in change detection include background fluctuations, illumination variation, weather changes, intermittent object motion, shadow, fast/slow object motion, camera motion, heterogeneous object shapes and realtime processing. Traditionally, this problem has been solved using hand-crafted features and background modelling techniques. In recent years, deep learning frameworks have been successfully adopted for robust change detection. This article aims to provide an empirical review of the state-of-the-art deep learning methods for change detection. More specifically, we present a detailed analysis of the technical characteristics of different model designs and experimental frameworks. We provide model design based categorization of the existing approaches, including the 2D-CNN, 3D-CNN, ConvLSTM, multi-scale features, residual connections, autoencoders and GAN based methods. Moreover, an empirical analysis of the evaluation settings adopted by the existing deep learning methods is presented. To the best of our knowledge, this is a first attempt to comparatively analyze the different evaluation frameworks used in the existing deep change detection methods. Finally, we point out the research needs, future directions and draw our own conclusions.

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Section: Deep Learning Models For Change Detectionmentioning

confidence: 99%

An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs

Mandal¹,

Vipparthi²

2021

Preprint

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“…Dempster-Schafer models [54], subspace learning models [55,56,57,58,59], robust learning models [60,61,62,63], neural networks models [64,65,66] and filter based models [67,68,69,70].…”

Section: Background Modelingmentioning

confidence: 99%

“…Subspace learning models handle illumination changes more robustly than statistical models [21]. In further approaches, discriminative [56,57,58] and mixed [59] subspace learning models have been used to increase the performance for foreground detection. However, each of these regular subspace methods presents a high sensitivity to noise, outliers, and missing data.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Moving Objects Detection with a Moving Camera: A Comprehensive Review

Chapel¹,

Bouwmans²

2020

Preprint

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During about 30 years, a lot of research teams have worked on the big challenge of detection of moving objects in various challenging environments. First applications concern static cameras but with the rise of the mobile sensors studies on moving cameras have emerged over time. In this survey, we propose to identify and categorize the different existing methods found in the literature. For this purpose, we propose to classify these methods according to the choose of the scene representation: one plane or several parts. Inside these two categories, the methods are grouped according to eight different approaches: panoramic background subtraction, dual cameras, motion compensation, subspace segmentation, motion segmentation, plane+parallax, multi planes and split image in blocks. A reminder of methods for static cameras is provided as well as the challenges with both static and moving cameras. Publicly available datasets and evaluation metrics are also surveyed in this paper.

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“…Background-foreground separation in a video taken by a static camera is a crucial step for detecting moving objects in the video surveillance systems [25], [26], [29]. Before the work of Candès in 2009, this task was usually addressed by statistical modeling [244], [251], [260], fuzzy modeling [15], [16], [17], [27] and conventional subspace learning model either reconstructive [65], [66], [162], [239], [207], [253], [282], [321] and discriminative [84], [85], [192]. However, RPCA methods immediately provided a very promising solution towards moving object detection.…”

Section: A Background-foreground Separationmentioning

confidence: 99%

On the Applications of Robust PCA in Image and Video Processing

et al. 2018

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Robust PCA (RPCA) via decomposition into lowrank plus sparse matrices offers a powerful framework for a large variety of applications such as image processing, video processing and 3D computer vision. Indeed, most of the time these applications require to detect sparse outliers from the observed imagery data that can be approximated by a lowrank matrix. Moreover, most of the time experiments show that RPCA with additional spatial and/or temporal constraints often outperforms the state-of-the-art algorithms in these applications. Thus, the aim of this paper is to survey the applications of RPCA in computer vision. In the first part of this paper, we review representative image processing applications as follows: (1) lowlevel imaging such as image recovery and denoising, image composition, image colorization, image alignment and rectification, multi-focus image and face recognition, (2) medical imaging like dynamic Magnetic Resonance Imaging (MRI) for acceleration of data acquisition, background suppression and learning of inter-frame motion fields, and (3) imaging for 3D computer vision with additional depth information like in Structure from Motion (SfM) and 3D motion recovery. In the second part, we present the applications of RPCA in video processing which utilize additional spatial and temporal information compared to image processing. Specifically, we investigate video denoising and restoration, hyperspectral video and background/foreground separation. Finally, we provide perspectives on possible future research directions and algorithmic frameworks that are suitable for these applications.

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Background Modeling via Incremental Maximum Margin Criterion

Cited by 6 publications

References 33 publications

An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs

An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs

Moving Objects Detection with a Moving Camera: A Comprehensive Review

On the Applications of Robust PCA in Image and Video Processing

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