Particle Filtering for Large Dimensional State Spaces with Multimodal Observation Likelihoods

We study efficient importance sampling techniques for particle filtering (PF) when either (a) the observation likelihood (OL) is frequently multimodal or heavy-tailed, or (b) the state space dimension is large or both. When the OL is multimodal, but the state transition pdf (STP) is narrow enough, the optimal importance density is usually unimodal. Under this assumption, many techniques have been proposed. But when the STP is broad, this assumption does not hold. We study how existing techniques can be generalized to situations where the optimal importance density is multimodal, but is unimodal conditioned on a part of the state vector. Sufficient conditions to test for the unimodality of this conditional posterior are derived. The number of particles, N, to accurately track using a PF increases with state space dimension, thus making any regular PF impractical for large dimensional tracking problems. We propose a solution that partially addresses this problem. An important class of large dimensional problems with multimodal OL is tracking spatially varying physical quantities such as temperature or pressure in a large area using a network of sensors which may be nonlinear and/or may have non-negligible failure probabilities.Comment: To appear in IEEE Trans. Signal Processin

Tracking interacting targets in multi-modal sensors

PhDObject tracking is one of the fundamental tasks in various applications such as surveillance, sports, video conferencing and activity recognition. Factors such as occlusions, illumination changes and limited field of observance of the sensor make tracking a challenging task. To overcome these challenges the focus of this thesis is on using multiple modalities such as audio and video for multi-target, multi-modal tracking. Particularly, this thesis presents contributions to four related research topics, namely, pre-processing of input signals to reduce noise, multi-modal tracking, simultaneous detection and tracking, and interaction recognition. To improve the performance of detection algorithms, especially in the presence of noise, this thesis investigate filtering of the input data through spatio-temporal feature analysis as well as through frequency band analysis. The pre-processed data from multiple modalities is then fused within Particle filtering (PF). To further minimise the discrepancy between the real and the estimated positions, we propose a strategy that associates the hypotheses and the measurements with a real target, using a Weighted Probabilistic Data Association (WPDA). Since the filtering involved in the detection process reduces the available information and is inapplicable on low signal-to-noise ratio data, we investigate simultaneous detection and tracking approaches and propose a multi-target track-beforedetect Particle filtering (MT-TBD-PF). The proposed MT-TBD-PF algorithm bypasses the detection step and performs tracking in the raw signal. Finally, we apply the proposed multi-modal tracking to recognise interactions between targets in regions within, as well as outside the cameras’ fields of view. The efficiency of the proposed approaches are demonstrated on large uni-modal, multi-modal and multi-sensor scenarios from real world detections, tracking and event recognition datasets and through participation in evaluation campaigns

Suivi Multi-Locuteurs avec des Informations Audio-Visuelles pour la Perception des Robots

Robot perception plays a crucial role in human-robot interaction (HRI). Perception system provides the robot information of the surroundings and enables the robot to give feedbacks. In a conversational scenario, a group of people may chat in front of the robot and move freely. In such situations, robots are expected to understand where are the people, who are speaking, or what are they talking about. This thesis concentrates on answering the first two questions, namely speaker tracking and diarization. We use different modalities of the robot’s perception system to achieve the goal. Like seeing and hearing for a human-being, audio and visual information are the critical cues for a robot in a conversational scenario. The advancement of computer vision and audio processing of the last decade has revolutionized the robot perception abilities. In this thesis, we have the following contributions: we first develop a variational Bayesian framework for tracking multiple objects. The variational Bayesian framework gives closed-form tractable problem solutions, which makes the tracking process efficient. The framework is first applied to visual multiple-person tracking. Birth and death process are built jointly with the framework to deal with the varying number of the people in the scene. Furthermore, we exploit the complementarity of vision and robot motorinformation. On the one hand, the robot’s active motion can be integrated into the visual tracking system to stabilize the tracking. On the other hand, visual information can be used to perform motor servoing. Moreover, audio and visual information are then combined in the variational framework, to estimate the smooth trajectories of speaking people, and to infer the acoustic status of a person- speaking or silent. In addition, we employ the model to acoustic-only speaker localization and tracking. Online dereverberation techniques are first applied then followed by the tracking system. Finally, a variant of the acoustic speaker tracking model based on von-Mises distribution is proposed, which is specifically adapted to directional data. All the proposed methods are validated on datasets according to applications.La perception des robots joue un rôle crucial dans l’interaction homme-robot (HRI). Le système de perception fournit les informations au robot sur l’environnement, ce qui permet au robot de réagir en consequence. Dans un scénario de conversation, un groupe de personnes peut discuter devant le robot et se déplacer librement. Dans de telles situations, les robots sont censés comprendre où sont les gens, ceux qui parlent et de quoi ils parlent. Cette thèse se concentre sur les deux premières questions, à savoir le suivi et la diarisation des locuteurs. Nous utilisons différentes modalités du système de perception du robot pour remplir cet objectif. Comme pour l’humain, l’ouie et la vue sont essentielles pour un robot dans un scénario de conversation. Les progrès de la vision par ordinateur et du traitement audio de la dernière décennie ont révolutionné les capacités de perception des robots. Dans cette thèse, nous développons les contributions suivantes : nous développons d’abord un cadre variationnel bayésien pour suivre plusieurs objets. Le cadre bayésien variationnel fournit des solutions explicites, rendant le processus de suivi très efficace. Cette approche est d’abord appliqué au suivi visuel de plusieurs personnes. Les processus de créations et de destructions sont en adéquation avecle modèle probabiliste proposé pour traiter un nombre variable de personnes. De plus, nous exploitons la complémentarité de la vision et des informations du moteur du robot : d’une part, le mouvement actif du robot peut être intégré au système de suivi visuel pour le stabiliser ; d’autre part, les informations visuelles peuvent être utilisées pour effectuer l’asservissement du moteur. Par la suite, les informations audio et visuelles sont combinées dans le modèle variationnel, pour lisser les trajectoires et déduire le statut acoustique d’une personne : parlant ou silencieux. Pour experimenter un scenario où l’informationvisuelle est absente, nous essayons le modèle pour la localisation et le suivi des locuteurs basé sur l’information acoustique uniquement. Les techniques de déréverbération sont d’abord appliquées, dont le résultat est fourni au système de suivi. Enfin, une variante du modèle de suivi des locuteurs basée sur la distribution de von-Mises est proposée, celle-ci étant plus adaptée aux données directionnelles. Toutes les méthodes proposées sont validées sur des bases de données specifiques à chaque application

Low and Variable Frame Rate Face Tracking Using an IP PTZ Camera

RÉSUMÉ En vision par ordinateur, le suivi d'objets avec des caméras PTZ a des applications dans divers domaines, tels que la surveillance vidéo, la surveillance du trafic, la surveillance de personnes et la reconnaissance de visage. Toutefois, un suivi plus précis, efficace, et fiable est requis pour une utilisation courante dans ces domaines. Dans cette thèse, le suivi est appliqué au haut du corps d'un humain, en incluant son visage. Le suivi du visage permet de déterminer son emplacement pour chaque trame d'une vidéo. Il peut être utilisé pour obtenir des images du visage d'un humain dans des poses différentes. Dans ce travail, nous proposons de suivre le visage d'un humain à l’aide d'une caméra IP PTZ (caméra réseau orientable). Une caméra IP PTZ répond à une commande via son serveur Web intégré et permet un accès distribué à partir d'Internet. Le suivi avec ce type de caméra inclut un bon nombre de défis, tels que des temps de réponse irrégulier aux commandes de contrôle, des taux de trame faibles et irréguliers, de grand mouvements de la cible entre deux trames, des occlusions, des modifications au champ de vue, des changements d'échelle, etc. Dans notre travail, nous souhaitons solutionner les problèmes des grands mouvements de la cible entre deux trames consécutives, du faible taux de trame, des modifications de l'arrière-plan, et du suivi avec divers changements d'échelle. En outre, l'algorithme de suivi doit prévoir les temps de réponse irréguliers de la caméra. Notre solution se compose d’une phase d’initialisation pour modéliser la cible (haut du corps), d’une adaptation du filtre de particules qui utilise le flux optique pour générer des échantillons à chaque trame (APF-OFS), et du contrôle de la caméra. Chaque composante exige des stratégies différentes. Lors de l'initialisation, on suppose que la caméra est statique. Ainsi, la détection du mouvement par soustraction d’arrière-plan est utilisée pour détecter l'emplacement initial de la personne. Ensuite, pour supprimer les faux positifs, un classificateur Bayesien est appliqué sur la région détectée afin de localiser les régions avec de la peau. Ensuite, une détection du visage basée sur la méthode de Viola et Jones est effectuée sur les régions de la peau. Si un visage est détecté, le suivi est lancé sur le haut du corps de la personne.----------ABSTRACT Object tracking with PTZ cameras has various applications in different computer vision topics such as video surveillance, traffic monitoring, people monitoring and face recognition. Accurate, efficient, and reliable tracking is required for this task. Here, object tracking is applied to human upper body tracking and face tracking. Face tracking determines the location of the human face for each input image of a video. It can be used to get images of the face of a human target under different poses. We propose to track the human face by means of an Internet Protocol (IP) Pan-Tilt-Zoom (PTZ) camera (i.e. a network-based camera that pans, tilts and zooms). An IP PTZ camera responds to command via its integrated web server. It allows a distributed access from Internet (access from everywhere, but with non-defined delay). Tracking with such camera includes many challenges such as irregular response times to camera control commands, low and irregular frame rate, large motions of the target between two frames, target occlusion, changing field of view (FOV), various scale changes, etc. In our work, we want to cope with the problem of large inter-frame motion of targets, low usable frame rate, background changes, and tracking with various scale changes. In addition, the tracking algorithm should handle the camera response time and zooming. Our solution consists of a system initialization phase which is the processing before camera motion and a tracker based on an Adaptive Particle Filter using Optical Flow based Sampling (APF-OFS) tracker, and camera control that are the processing after the motion of the camera. Each part requires different strategies. For initialization, when the camera is stationary, motion detection for a static camera is used to detect the initial location of the person face entering an area. For motion detection in the FOV of the camera, a background subtraction method is applied. Then to remove false positives, Bayesian skin classifier is applied on the detected motion region to discriminate skin regions from non skin regions. Face detection based on Viola and Jones face detector can be performed on the detected skin regions independently of their face size and position within the image

Visual tracking over multiple temporal scales

Visual tracking is the task of repeatedly inferring the state (position, motion, etc.) of the desired target in an image sequence. It is an important scientific problem as humans can visually track targets in a broad range of settings. However, visual tracking algorithms struggle to robustly follow a target in unconstrained scenarios. Among the many challenges faced by visual trackers, two important ones are occlusions and abrupt motion variations. Occlusions take place when (an)other object(s) obscures the camera's view of the tracked target. A target may exhibit abrupt variations in apparent motion due to its own unexpected movement, camera movement, and low frame rate image acquisition. Each of these issues can cause a tracker to lose its target. This thesis introduces the idea of learning and propagation of tracking information over multiple temporal scales to overcome occlusions and abrupt motion variations. A temporal scale is a specific sequence of moments in time Models (describing appearance and/or motion of the target) can be learned from the target tracking history over multiple temporal scales and applied over multiple temporal scales in the future. With the rise of multiple motion model tracking frameworks, there is a need for a broad range of search methods and ways of selecting between the available motion models. The potential benefits of learning over multiple temporal scales are first assessed by studying both motion and appearance variations in the ground-truth data associated with several image sequences. A visual tracker operating over multiple temporal scales is then proposed that is capable of handling occlusions and abrupt motion variations. Experiments are performed to compare the performance of the tracker with competing methods, and to analyze the impact on performance of various elements of the proposed approach. Results reveal a simple, yet general framework for dealing with occlusions and abrupt motion variations. In refining the proposed framework, a search method is generalized for multiple competing hypotheses in visual tracking, and a new motion model selection criterion is proposed

Articulated human tracking and behavioural analysis in video sequences

Recently, there has been a dramatic growth of interest in the observation and tracking of human subjects through video sequences. Arguably, the principal impetus has come from the perceived demand for technological surveillance, however applications in entertainment, intelligent domiciles and medicine are also increasing. This thesis examines human articulated tracking and the classi cation of human movement, rst separately and then as a sequential process. First, this thesis considers the development and training of a 3D model of human body structure and dynamics. To process video sequences, an observation model is also designed with a multi-component likelihood based on edge, silhouette and colour. This is de ned on the articulated limbs, and visible from a single or multiple cameras, each of which may be calibrated from that sequence. Second, for behavioural analysis, we develop a methodology in which actions and activities are described by semantic labels generated from a Movement Cluster Model (MCM). Third, a Hierarchical Partitioned Particle Filter (HPPF) was developed for human tracking that allows multi-level parameter search consistent with the body structure. This tracker relies on the articulated motion prediction provided by the MCM at pose or limb level. Fourth, tracking and movement analysis are integrated to generate a probabilistic activity description with action labels. The implemented algorithms for tracking and behavioural analysis are tested extensively and independently against ground truth on human tracking and surveillance datasets. Dynamic models are shown to predict and generate synthetic motion, while MCM recovers both periodic and non-periodic activities, de ned either on the whole body or at the limb level. Tracking results are comparable with the state of the art, however the integrated behaviour analysis adds to the value of the approach.Overseas Research Students Awards Scheme (ORSAS