References of "Braham, Marc"
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See detailSemantic Background Subtraction
Braham, Marc ULg; Pierard, Sébastien ULg; Van Droogenbroeck, Marc ULg

in IEEE International Conference on Image Processing (ICIP), Beijing 17-20 September 2017 (2017, September)

We introduce the notion of semantic background subtraction, a novel framework for motion detection in video sequences. The key innovation consists to leverage object-level semantics to address the variety ... [more ▼]

We introduce the notion of semantic background subtraction, a novel framework for motion detection in video sequences. The key innovation consists to leverage object-level semantics to address the variety of challenging scenarios for background subtraction. Our framework combines the information of a semantic segmentation algorithm, expressed by a probability for each pixel, with the output of any background subtraction algorithm to reduce false positive detections produced by illumination changes, dynamic backgrounds, strong shadows, and ghosts. In addition, it maintains a fully semantic background model to improve the detection of camouflaged foreground objects. Experiments led on the CDNet dataset show that we managed to improve, significantly, almost all background subtraction algorithms of the CDNet leaderboard, and reduce the mean overall error rate of all the 34 algorithms (resp. of the best 5 algorithms) by roughly 50% (resp. 20%). Note that a C++ implementation of the framework is available at http://www.telecom.ulg.ac.be/semantic. [less ▲]

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See detailDeep Background Subtraction with Scene-Specific Convolutional Neural Networks
Braham, Marc ULg; Van Droogenbroeck, Marc ULg

in IEEE International Conference on Systems, Signals and Image Processing (IWSSIP), Bratislava 23-25 May 2016 (2016, May)

Background subtraction is usually based on low-level or hand-crafted features such as raw color components, gradients, or local binary patterns. As an improvement, we present a background subtraction ... [more ▼]

Background subtraction is usually based on low-level or hand-crafted features such as raw color components, gradients, or local binary patterns. As an improvement, we present a background subtraction algorithm based on spatial features learned with convolutional neural networks (ConvNets). Our algorithm uses a background model reduced to a single background image and a scene-specific training dataset to feed ConvNets that prove able to learn how to subtract the background from an input image patch. Experiments led on 2014 ChangeDetection.net dataset show that our ConvNet based algorithm at least reproduces the performance of state-of-the-art methods, and that it even outperforms them significantly when scene-specific knowledge is considered. [less ▲]

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See detailA Generic Feature Selection Method for Background Subtraction Using Global Foreground Models
Braham, Marc ULg; Van Droogenbroeck, Marc ULg

in Advanced Concepts for Intelligent Vision Systems (ACIVS), Catania 26-29 October 2015 (2015, October)

Over the last few years, a wide variety of background subtraction algorithms have been proposed for the detection of moving objects in videos acquired with a static camera. While much effort have been ... [more ▼]

Over the last few years, a wide variety of background subtraction algorithms have been proposed for the detection of moving objects in videos acquired with a static camera. While much effort have been devoted to the development of robust background models, the automatic spatial selection of useful features for representing the background has been neglected. In this paper, we propose a generic and tractable feature selection method. Interesting contributions of this work are the proposal of a selection process coherent with the segmentation process and the exploitation of global foreground models in the selection strategy. Experiments conducted on the ViBe algorithm show that our feature selection technique improves the segmentation results. [less ▲]

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See detailSimple Median-Based Method for Stationary Background Generation Using Background Subtraction Algorithms
Laugraud, Benjamin ULg; Pierard, Sébastien ULg; Braham, Marc ULg et al

in New Trends in Image Analysis and Processing - ICIAP 2015 Workshops (2015, September)

The estimation of the background image from a video sequence is necessary in some applications. Computing the median for each pixel over time is effective, but it fails when the background is visible for ... [more ▼]

The estimation of the background image from a video sequence is necessary in some applications. Computing the median for each pixel over time is effective, but it fails when the background is visible for less than half of the time. In this paper, we propose a new method leveraging the segmentation performed by a background subtraction algorithm, which reduces the set of color candidates, for each pixel, before the median is applied. Our method is simple and fully generic as any background subtraction algorithm can be used. While recent background subtraction algorithms are excellent in detecting moving objects, our experiments show that the frame difference algorithm is a technique that compare advantageously to more advanced ones. Finally, we present the background images obtained on the SBI dataset, which appear to be almost perfect. The source code of our method can be downloaded at http://www.ulg.ac.be/telecom/research/sbg. [less ▲]

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See detailA physically motivated pixel-based model for background subtraction in 3D images
Braham, Marc ULg; Lejeune, Antoine ULg; Van Droogenbroeck, Marc ULg

in IEEE International Conference on 3D Imaging (IC3D), Liège 9-10 December 2014 (2014, December)

This paper proposes a new pixel-based background subtraction technique, applicable to range images, to detect motion. Our method exploits the physical meaning of depth information, which leads to an ... [more ▼]

This paper proposes a new pixel-based background subtraction technique, applicable to range images, to detect motion. Our method exploits the physical meaning of depth information, which leads to an improved background/foreground segmentation and the instantaneous suppression of ghosts that would appear on color images. In particular, our technique considers certain characteristics of depth measurements, such as failures for certain pixels or the non-uniformity of the spatial distribution of noise in range images, to build an improved pixel-based background model. Experiments show that incorporating specificities related to depth measurements allows us to propose a method whose performance is increased with respect to other state-of-the-art methods. [less ▲]

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