Robust Principal Component Analysis for Background Subtraction: Systematic Evaluation and Comparative Analysis

The analysis and understanding of video sequences is currently quite an active research field. Many applications such as video surveillance, optical motion capture or those of multimedia need to first be able to detect the objects moving in a scene filmed by a static camera. This requires the basic operation that consists of separating the moving objects called "foreground" from the static information called "background". Many background subtraction methods have been developed (Bouwmans et al. (2010); Bouwmans et al. (2008)). A recent survey (Bouwmans (2009)) shows that subspace learning models are well suited for background subtraction. Principal Component Analysis (PCA) has been used to model the background by significantly reducing the data's dimension. To perform PCA, different Robust Principal Components Analysis (RPCA) models have been recently developed in the literature. The background sequence is then modeled by a low rank subspace that can gradually change over time, while the moving foreground objects constitute the correlated sparse outliers. However, authors compare their algorithm only with the PCA (Oliver et al. (1999)) or another RPCA model. Furthermore, the evaluation is not made with the datasets and the measures currently used in the field of background subtraction. Considering all of this, we propose to evaluate RPCA models in the field of video-surveillance. Contributions of this chapter can be summarized as follows: 1) A survey regarding robust principal component analysis and 2) An evaluation and comparison on different video surveillance dataset

Fuzzy Object retrieval by using histogram of fuzzy Allen relations

Abstract-Relative position of object description are widely used in event understanding and computer vision tasks especially in object recognition. Use of low level features cannot give satisfactory results when high level concepts is not easily expressible in low level contents. Mostly researchers are concentrating on spatio-temporal relationship between objects or regions of an object in images. Object retrieval which is taken into account the relative position of objects in images become important. In such a case classical Allen relations are used. Searched object can take various shapes and scale according to shooting. Fuzzy methods have the ability to compensate the imprecise informations and vagueness. In this paper fuzzy histograms of Allen relations are used for object retrieval. Fuzzy histograms of Allen relations are the quantitative representation of relative object position. For this purpose Matsakis's [9] algorithm for fuzzification of line segments is refined. This representation is affine invariant. Query is made by example and only corresponding relative relation between objects is considered. Results are analyzed by a well known Receiver Operating Characteristic curve ( ROC )method

Indexing and retrieval based on spatial relationships between objects

In the context of image database retrieval, users often formulate queries based on the content of images modeled in terms of different features such as shape, color or texture etc. Another type of these queries may be expressed in terms of spatial arrangement of objects. In this paper, we focus on spatial relationships modeling among objects extracted from images. The introduced approach preserves objects properties (form, shape, etc.) and exploits topological and directional information.L'accès à une base de données d'images s'effectue généralement selon des requêtes fondées entre autres sur des indices visuels apparents (la forme, la couleur, la texture, etc.), qui ont pour but de définir une similarité entre celles-ci et le reste des images de la base. Dans ce travail, nous nous intéressons à la modélisation du contenu d'images en terme de relations spatiales existantes entre les différents objets extraits de celles-ci. L'approche proposée utilise conjointement les relations topologiques et d'orientation et préserve les propriétés intrinsèques des objets spatiaux

Human Pose Estimation from Monocular Images : a Comprehensive Survey

Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problema into several modules: feature extraction and description, human body models, and modelin methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used

Two-Dimensional Fuzzy Spatial Relations: A New Way of Computing and Representation

Fuzziness is found everywhere, in modeling spatial relations, fuzziness is found at object level as well as in relation semantics. Commonly, fuzzy topological relations are computed between fuzzy objects. Fuzziness in relation semantics is represented by fuzzy topological relations between crisp objects and these types of fuzzy topological relations are much less developed. In this paper, we propose a method for combining fuzzy topological and directional relations. We also propose an algorithm for defuzzification of relations which provides us a binary topological and directional relation between a 2D object pair. These relations are represented in a neighborhood graph. For validation and assessment, a number of experiments have been performed on artificial data

Fuzzy Difference Operators for Spatial Change Detection in 2D Spatial Scene

Fuzzy difference operators are used in different fields of decision making processes, image analysis and computer vision applications. Change detection in a spatial scene is the basic issue in modeling relative motion between object pair. Many approaches are adopted to change detection in a spatial scene and change in spatial relations is one of them. Separate methodologies are adapted to determine the change in topological, directional and distance relations. In this paper, a methodology for detection of a spatial change based on fuzzy matrix calculus is presented. Difference of combined fuzzy topological and directional relations matrices is determined by fuzzy difference operators. Experiments are performed to validate the proposed method and the promising results are obtained

Foreground Detection by Robust PCA solved via a Linearized Alternating Direction Method

Robust Principal Components Analysis (RPCA) shows a nice framework to separate moving objects from the background. The background sequence is then modeled by a low rank subspace that can gradually change over time, while the moving foreground objects constitute the correlated sparse outliers. RPCA problem can be exactly solved via convex optimization that minimizes a combination of the nuclear norm and the l1-norm. To solve this convex program, an Alternating Direction Method (ADM) is commonly used. However, the subproblems in ADM are easily solvable only when the linear mappings in the constraints are identities. This assumption is rarely verified in real application such as foreground detection. In this paper, we propose to use a Linearized Alternating Direction Method (LADM) with adaptive penalty to achieve RPCA for foreground detection. LADM alleviates the constraints of the original ADM with a faster convergence speed. Experimental results on different datasets show the pertinence of the proposed approach