Flexible test-bed for unusual behavior detection

Visual surveillance and activity analysis is an active research field of computer vision. As a result, there are several different algorithms produced for this purpose. To obtain more robust systems it is desirable to integrate the different algorithms. To help achieve this goal, we propose a flexible, distributed software collaboration framework and present a prototype system for automatic event analysis. Copyright 2007 ACM

Tracking moving objects in surveillance video

The thesis looks at approaches to the detection and tracking of potential objects of interest in surveillance video. The aim was to investigate and develop methods that might be suitable for eventual application through embedded software, running on a fixed-point processor, in analytics capable cameras. The work considers common approaches to object detection and representation, seeking out those that offer the necessary computational economy and the potential to be able to cope with constraints such as low frame rate due to possible limited processor time, or weak chromatic content that can occur in some typical surveillance contexts. The aim is for probabilistic tracking of objects rather than simple concatenation of frame by frame detections. This involves using recursive Bayesian estimation. The particle filter is a technique for implementing such a recursion and so it is examined in the context of both single target and combined multi-target tracking. A detailed examination of the operation of the single target tracking particle filter shows that objects can be tracked successfully using a relatively simple structured grey-scale histogram representation. It is shown that basic components of the particle filter can be simplified without loss in tracking quality. An analysis brings out the relationships between commonly used target representation distance measures and shows that in the context of the particle filter there is little to choose between them. With the correct choice of parameters, the simplest and computationally economic distance measure performs well. The work shows how to make that correct choice. Similarly, it is shown that a simple measurement likelihood function can be used in place of the more ubiquitous Gaussian. The important step of target state estimation is examined. The standard weighted mean approach is rejected, a recently proposed maximum a posteriori approach is shown to be not suitable in the context of the work, and a practical alternative is developed. Two methods are presented for tracker initialization. One of them is a simplification of an existing published method, the other is a novel approach. The aim is to detect trackable objects as they enter the scene, extract trackable features, then actively follow those features through subsequent frames. The multi-target tracking problem is then posed as one of management of multiple independent trackers

Compréhension de contenus visuels par analyse conjointe du contenu et des usages

Dans cette thèse, nous traitons de la compréhension de contenus visuels, qu’il s’agisse d’images, de vidéos ou encore de contenus 3D. On entend par compréhension la capacité à inférer des informations sémantiques sur le contenu visuel. L’objectif de ce travail est d’étudier des méthodes combinant deux approches : 1) l’analyse automatique des contenus et 2) l’analyse des interactions liées à l’utilisation de ces contenus (analyse des usages, en plus bref). Dans un premier temps, nous étudions l’état de l’art issu des communautés de la vision par ordinateur et du multimédia. Il y a 20 ans, l’approche dominante visait une compréhension complètement automatique des images. Cette approche laisse aujourd’hui plus de place à différentes formes d’interventions humaines. Ces dernières peuvent se traduire par la constitution d’une base d’apprentissage annotée, par la résolution interactive de problèmes (par exemple de détection ou de segmentation) ou encore par la collecte d’informations implicites issues des usages du contenu. Il existe des liens riches et complexes entre supervision humaine d’algorithmes automatiques et adaptation des contributions humaines via la mise en œuvre d’algorithmes automatiques. Ces liens sont à l’origine de questions de recherche modernes : comment motiver des intervenants humains ? Comment concevoir des scénarii interactifs pour lesquels les interactions contribuent à comprendre le contenu manipulé ? Comment vérifier la qualité des traces collectées ? Comment agréger les données d’usage ? Comment fusionner les données d’usage avec celles, plus classiques, issues d’une analyse automatique ? Notre revue de la littérature aborde ces questions et permet de positionner les contributions de cette thèse. Celles-ci s’articulent en deux grandes parties. La première partie de nos travaux revisite la détection de régions importantes ou saillantes au travers de retours implicites d’utilisateurs qui visualisent ou acquièrent des con- tenus visuels. En 2D d’abord, plusieurs interfaces de vidéos interactives (en particulier la vidéo zoomable) sont conçues pour coordonner des analyses basées sur le contenu avec celles basées sur l’usage. On généralise ces résultats en 3D avec l’introduction d’un nouveau détecteur de régions saillantes déduit de la capture simultanée de vidéos de la même performance artistique publique (spectacles de danse, de chant etc.) par de nombreux utilisateurs. La seconde contribution de notre travail vise une compréhension sémantique d’images fixes. Nous exploitons les données récoltées à travers un jeu, Ask’nSeek, que nous avons créé. Les interactions élémentaires (comme les clics) et les données textuelles saisies par les joueurs sont, comme précédemment, rapprochées d’analyses automatiques des images. Nous montrons en particulier l’intérêt d’interactions révélatrices des relations spatiales entre différents objets détectables dans une même scène. Après la détection des objets d’intérêt dans une scène, nous abordons aussi le problème, plus ambitieux, de la segmentation. ABSTRACT : This thesis focuses on the problem of understanding visual contents, which can be images, videos or 3D contents. Understanding means that we aim at inferring semantic information about the visual content. The goal of our work is to study methods that combine two types of approaches: 1) automatic content analysis and 2) an analysis of how humans interact with the content (in other words, usage analysis). We start by reviewing the state of the art from both Computer Vision and Multimedia communities. Twenty years ago, the main approach was aiming at a fully automatic understanding of images. This approach today gives way to different forms of human intervention, whether it is through the constitution of annotated datasets, or by solving problems interactively (e.g. detection or segmentation), or by the implicit collection of information gathered from content usages. These different types of human intervention are at the heart of modern research questions: how to motivate human contributors? How to design interactive scenarii that will generate interactions that contribute to content understanding? How to check or ensure the quality of human contributions? How to aggregate human contributions? How to fuse inputs obtained from usage analysis with traditional outputs from content analysis? Our literature review addresses these questions and allows us to position the contributions of this thesis. In our first set of contributions we revisit the detection of important (or salient) regions through implicit feedback from users that either consume or produce visual contents. In 2D, we develop several interfaces of interactive video (e.g. zoomable video) in order to coordinate content analysis and usage analysis. We also generalize these results to 3D by introducing a new detector of salient regions that builds upon simultaneous video recordings of the same public artistic performance (dance show, chant, etc.) by multiple users. The second contribution of our work aims at a semantic understanding of fixed images. With this goal in mind, we use data gathered through a game, Ask’nSeek, that we created. Elementary interactions (such as clicks) together with textual input data from players are, as before, mixed with automatic analysis of images. In particular, we show the usefulness of interactions that help revealing spatial relations between different objects in a scene. After studying the problem of detecting objects on a scene, we also adress the more ambitious problem of segmentation

Human Tracking by Fast Mean Shift Mode Seeking [10 font size blank 1]

[10 font size blank 1] [10 font size blank 2] [10 font size blank 3] Abstract — Change detection by background subtraction is a common approach to detect moving foreground. The resulting difference image is usually thresholded to obtain objects based on pixel connectedness and resulting blob objects are subsequently tracked. This paper proposes a detection approach not requiring the binarization of the difference image. Local density maxima in the difference image- usually representing moving objects- are outlined by a fast non-parametric mean shift clustering procedure. Object tracking is carried out by updating and propagating cluster parameters over time using the mode seeking property of the mean shift procedure. For occluding targets, a fast procedure determining the object configuration maximizing image likelihood is presented. Detection and tracking results are demonstrated for a crowded scene and evaluation of the proposed tracking framework is presented. [9 font size blank 1] Index Terms—automated visual surveillance, motion detection, mean shift clustering, human tracking, occlusion handling [9 font size blank 1] I