Marc Van Droogenbroeck

ViBE: A powerful random technique to estimate the background in video sequences

Background subtraction is a crucial step in many automatic video content analysis applications. While numerous acceptable techniques have been proposed so far for background extraction, there is still a need to produce more efficient algorithms in terms of adaptability to multiple environments, noise resilience, and computation efficiency. In this paper, we present a powerful method for background extraction that improves in accuracy and reduces the computational load. The main innovation concerns the use of a random policy to select values to build a samples-based estimation of the background. To our knowledge, it is the first time that a random aggregation is used in the field of background extraction. In addition we propose a novel policy that propagates information between neighboring pixels of an image. Experiment detailed in this paper show how our method improves on other widely used techniques, and how it outperforms these techniques for noisy images.

Fast computation of morphological operations with arbitrary structuring elements

Abstract This paper presents a general algorithm that performs basic mathematical morphology operations, like erosions and openings, with any arbitrary shaped structuring element in an efficient way. It is shown that our algorithm has a lower or equal complexity but better computing time than all comparable known methods.

Combining Color, Depth, and Motion for Video Segmentation

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.

Morphological Erosions and Openings: Fast Algorithms Based on Anchors

Several efficient algorithms for computing erosions and openings have been proposed recently. They improve on van Herk’s algorithm in terms of number of comparisons for large structuring elements. In this paper we introduce a theoretical framework of anchors that aims at a better understanding of the process involved in the computation of erosions and openings. It is shown that the knowledge of opening anchors of a signal f is sufficient to perform both the erosion and the opening of f.Then we propose an algorithm for one-dimensional erosions and openings which exploits opening anchors. This algorithm improves on the fastest algorithms available in literature by approximately 30% in terms of computation speed, for a range of structuring element sizes and image contents.

Frontal-view gait recognition by intra- and inter-frame rectangle size distribution

Current trends seem to accredit gait as a sensible biometric feature for human identification, at least in a multimodal system. In addition to being a robust feature, gait is hard to fake and requires no cooperation from the user. As in many video systems, the recognition confidence relies on the angle of view of the camera and on the illumination conditions, inducing a sensitivity to operational conditions that one may wish to lower. In this paper we present an efficient approach capable of recognizing people in frontal-view video sequences. The approach uses an intra-frame description of silhouettes which consists of a set of rectangles that will fit into any closed silhouette. A dynamic, inter-frame, dimension is then added by aggregating the size distributions of these rectangles over multiple successive frames. For each new frame, the inter-frame gait signature is updated and used to estimate the identity of the person detected in the scene. Finally, in order to smooth the decision on the identity, a majority vote is applied to previous results. In the final part of this article, we provide experimental results and discuss the accuracy of the classification for our own database of 21 known persons, and for a public database of 25 persons.

Deep background subtraction with scene-specific convolutional neural networks

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.

Robust analysis of silhouettes by morphological size distributions

We address the topic of real-time analysis and recognition of silhouettes. The method that we propose first produces object features obtained by a new type of morphological operators, which can be seen as an extension of existing granulometric filters, and then insert them into a tailored classification scheme. Intuitively, given a binary segmented image, our operator produces the set of all the largest rectangles that can be wedged inside any connected component of the image. The latter are obtained by a standard background subtraction technique and morphological filtering. To classify connected components into one of the known object categories, the rectangles of a connected component are submitted to a machine learning algorithm called EXtremely RAndomized trees (Extra-trees). The machine learning algorithm is fed with a static database of silhouettes that contains both positive and negative instances. The whole process, including image processing and rectangle classification, is carried out in real-time. Finally we evaluate our approach on one of todays hot topics: the detection of human silhouettes. We discuss experimental results and show that our method is stable and computationally effective. Therefore, we assess that algorithms like ours introduce new ways for the detection of humans in video sequences.

An evaluation of pixel-based methods for the detection of floating objects on the sea surface

Ship-based automatic detection of small floating objects on an agitated sea surface remains a hard problem. Our main concern is the detection of floating mines, which proved a real threat to shipping in confined waterways during the first Gulf War, but applications include salvaging, search-and-rescue operation, perimeter, or harbour defense. Detection in infrared (IR) is challenging because a rough sea is seen as a dynamic background of moving objects with size order, shape, and temperature similar to those of the floating mine. In this paper we have applied a selection of background subtraction algorithms to the problem, and we show that the recent algorithms such as ViBe and behaviour subtraction, which take into account spatial and temporal correlations within the dynamic scene, significantly outperform the more conventional parametric techniques, with only little prior assumptions about the physical properties of the scene.

Simple Median-Based Method for Stationary Background Generation Using Background Subtraction Algorithms

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.

BeAMS: A Beacon-Based Angle Measurement Sensor for Mobile Robot Positioning

Positioning is a fundamental issue in mobile robot applications, and it can be achieved in multiple ways. Among these methods, triangulation based on angle measurements is widely used, robust, accurate, and flexible. This paper presents BeAMS, which is a new active beacon-based angle measurement system used for mobile robot positioning. BeAMS introduces several major innovations. One innovation is the use of a unique unsynchronized channel with on-off keying modulated infrared signals to measure angles and to identify the beacons. We also introduce a new mechanism to measure angles: Our system detects a beacon when it enters and leaves an angular window. We show that the estimator resulting from the center of this angular window provides an unbiased estimate of the beacon angle. A theoretical framework for a thorough performance analysis of BeAMS is provided. We establish the upper bound of the variance and validate this bound through experiments and simulations; the overall error measure of BeAMS is lower than 0.24° for an acquisition rate of 10 Hz. In conclusion, BeAMS is a low-power, flexible, and robust solution for angle measurement and a reliable component for robot positioning.