Incremental Updating of 3D Topological Maps to Describe Videos

International audienceA topological map is an efficient mathematical model for representing an image subdivision where all cells and adjacency relations between elements are represented. It has been proved to be a very good tool for video processing when video is seen as a 3D image. However the construction of a topological map for representing a video needs the availability of the complete image sequence. In this paper we propose a procedure for online updating a topological map in order to build it as the video is produced, allowing to use it in real time

A Convolutional Neural Network into graph space

Convolutional neural networks (CNNs), in a few decades, have outperformed the existing state of the art methods in classification context. However, in the way they were formalised, CNNs are bound to operate on euclidean spaces. Indeed, convolution is a signal operation that are defined on euclidean spaces. This has restricted deep learning main use to euclidean-defined data such as sound or image. And yet, numerous computer application fields (among which network analysis, computational social science, chemo-informatics or computer graphics) induce non-euclideanly defined data such as graphs, networks or manifolds. In this paper we propose a new convolution neural network architecture, defined directly into graph space. Convolution and pooling operators are defined in graph domain. We show its usability in a back-propagation context. Experimental results show that our model performance is at state of the art level on simple tasks. It shows robustness with respect to graph domain changes and improvement with respect to other euclidean and non-euclidean convolutional architectures.Comment: arXiv admin note: text overlap with arXiv:1611.08402 by other author

A Graph-Kernel Method for Re-identification

Re-identification, that is recognizing that an object appearing in a scene is a reoccurrence of an object seen previously by the system (by the same camera or possibly by a different one) is a challenging problem in video surveillance. In this paper, the problem is addressed using a structural, graph-based representation of the objects of interest. A recently proposed graph kernel is adopted for extending to this representation the Principal Component Analyisis (PCA) technique. An experimental evaluation of the method has been performed on two video sequences from the publicly available PETS2009 database

An ensemble of rejecting classifiers for anomaly detection of audio events

Audio analytic systems are receiving an increasing interest in the scientific community, not only as stand alone systems for the automatic detection of abnormal events by the interpretation of the audio track, but also in conjunction with video analytics tools for enforcing the evidence of anomaly detection. In this paper we present an automatic recognizer of a set of abnormal audio events that works by extracting suitable features from the signals obtained by microphones installed into a surveilled area, and by classifying them using two classifiers that operate at different time resolutions. An original aspect of the proposed system is the estimation of the reliability of each response of the individual classifiers. In this way, each classifier is able to reject the samples having an overall reliability below a threshold. This approach allows our system to combine only reliable decisions, so increasing the overall performance of the method. The system has been tested on a large dataset of samples acquired from real world scenarios; the audio classes of interests are represented by gunshot, scream and glass breaking in addition to the background sounds. The preliminary results obtained encourage further research in this direction

An Experimental Evaluation of Foreground Detection Algorithms in Real Scenes

International audience; Foreground detection is an important preliminary step of many video analysis systems. Many algorithms have been proposed in the last years, but there is not yet a consensus on which approach is the most effective, not even limiting the problem to a single category of videos. This paper aims at constituting a first step towards a reliable assessment of the most commonly used approaches. In particular, four notable algorithms that perform foreground detection have been evaluated using quantitative measures to assess their relative merits and demerits. The evaluation has been carried out using a large, publicly available dataset composed by videos representing different realistic applicative scenarios. The obtained performance is presented and discussed, highlighting the conditions under which algorithm can represent the most effective solution

A Method for Counting People in Crowded Scenes

This paper presents a novel method to count people for video surveillance applications. Methods in the literature either follow a direct approach, by first detecting people and then counting them, or an indirect approach, by establishing a relation between some easily detectable scene features and the estimated number of people. The indirect approach is considerably more robust, but it is not easy to take into account such factors as perspective or people groups with different densities. The proposed technique, while based on the indirect approach, specifically addresses these problems; furthermore it is based on a trainable estimator that does not require an explicit formulation of a priori knowledge about the perspective and density effects present in the scene at hand. In the experimental evaluation, the method has been extensively compared with the algorithm by Albiol et al., which provided the highest performance at the PETS 2009 contest on people counting. The experimentation has used the public PETS 2009 datasets. The results confirm that the proposed method improves the accuracy, while retaining the robustness of the indirect approach

A Method for Counting Moving People in Video Surveillance Videos

International audience; People counting is an important problem in video surveillance applications. This problem has been faced either by trying to detect people in the scene and then counting them or by establishing a mapping between some scene feature and the number of people (avoiding the complex detection problem). This paper presents a novel method, following this second approach, that is based on the use of SURF features and of an https://static-content.springer.com/image/art%3A10.1155%2F2010%2F231240/MediaObjects/13634_2009_Article_2711_IEq1_HTML.gif -SVR regressor provide an estimate of this count. The algorithm takes specifically into account problems due to partial occlusions and to perspective. In the experimental evaluation, the proposed method has been compared with the algorithm by Albiol et al., winner of the PETS 2009 contest on people counting, using the same PETS 2009 database. The provided results confirm that the proposed method yields an improved accuracy, while retaining the robustness of Albiol's algorithm

Détection, suivi, et analyse de comportement des personnes en mouvement dans les systèmes de vidéo surveillance : une approche basée sur les graphes

Dans cette thèse, nous proposons un système de vidéo surveillance qui présente des nouveaux algorithmes de détection d\u27objets et de suivi d\u27objets, afin de pallier les principaux problèmes qui se présentent dans le développement de tels systèmes. Il a été proposé un nouvel algorithme de soustraction du fond, sélectif et adaptatif, pour adapter le système à des changements de luminosité et de la structure de la scène. En outre, pour rendre applicable le système à des environnements réels, des heuristiques ont été proposées pour la résolution des différents problèmes : ombres, bruit, etc. Les résultats produits sur la phase de détection d\u27objets montrent que les techniques proposées sont robustes et utilisables en temps réels grâce à un temps de calcul peu élevé. L\u27objet principal de la thèse a concerné la phase de suivi d\u27objets. Dans cette thèse, nous proposons un nouvel algorithme basé sur une expérimentation des objets qui utilisent les pyramides de graphes. Des tests expérimentaux sur des bases de données standard et sur des index attestés pour l\u27évaluation des algorithmes de suivi d\u27objets en présence d\u27occlusions montrent que cette approche est très prometteuse