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.

Abstract. This paper presents an innovative method to interpret the content of a video scene using a depth camera. Cameras that provide distance instead of color information are part of a promising young technology but they come with many difficulties: noisy signals, small resolution, and ambiguities, to cite a few.By taking advantage of the robustness to noise of a recent background subtraction algorithm, our method is able to extract useful information from the depth signals. We further enhance the robustness of the algorithm by combining this information with that of an RGB camera. In our experiments, we demonstrate this increased robustness and conclude by showing a practical example of an immersive application taking advantage of our algorithm.

From an engineering standpoint, both the skin and subcutaneous tissue act as interconnected load-transmitting structures. They are subject to a variety of intrinsic and environmental influences. Changes in the cutaneous viscoelasticity represent an important aspect in a series of skin conditions. The aim of this work was to explore the methodology of biomechanical measurements in order to better appreciate the evolution and severity of some connective tissue diseases. The Cutometer MPA 580 (C+K electronic) was used in the steep and progressive suction procedures. Adapting measurement modalities was explored in order to mitigate any variability in data collection. The repeat steep suction procedure conveniently reveals the creep phenomenon. By contrast, the progressive suction procedure highlights the hysteresis phenomenon. These viscoelastic characteristics are presently described using the 2 and 4 mm probes on normal skin and in scleroderma, acromegaly, corticosteroid-induced dermatoporosis, and Ehlers-Danlos syndrome. The apposition of an additional outer contention on the skin altered differently the manifestations of the creep extension and hysteresis among the tested skin conditions. Any change in the mechanical test procedure affects the data. In clinical and experimental settings, it is mandatory to adhere to a strict and controlled protocol.

Given a video sequence acquired with a fixed camera, the generation of the stationary background of the scene is a challenging problem which aims at computing a reference image for a motionless background. For that purpose, we developed our method named LaBGen, which emerged as the best one during the Scene Background Modeling and Initialization (SBMI) workshop organized in 2015, and the IEEE Scene Background Modeling Contest (SBMC) organized in 2016. LaBGen combines a pixel-wise temporal median filter and a patch selection mechanism based on motion detection. To detect motion, a background subtraction algorithm decides, for each frame, which pixels belong to the background. In this paper, we describe the LaBGen method extensively, evaluate it on the SBI 2016 dataset and compare its performance with other background generation methods. We also study its computational complexity, the performance sensitivity with respect to its parameters, and the stability of the predicted background image over time with respect to the chosen background subtraction algorithm.We provide an open source C++ implementation at http://www.telecom.ulg.ac.be/labgen.

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.