Multi-Bernoulli Sensor-Control via Minimization of Expected Estimation Errors

This paper presents a sensor-control method for choosing the best next state of the sensor(s), that provide(s) accurate estimation results in a multi-target tracking application. The proposed solution is formulated for a multi-Bernoulli filter and works via minimization of a new estimation error-based cost function. Simulation results demonstrate that the proposed method can outperform the state-of-the-art methods in terms of computation time and robustness to clutter while delivering similar accuracy

Tracking Multiple Persons Based on a Variational Bayesian Model

International audienceObject tracking is an ubiquitous problem in computer vision with many applications in human-machine and human-robot interaction, augmented reality, driving assistance, surveillance, etc. Although thoroughly investigated, tracking multiple persons remains a challenging and an open problem. In this paper, an online variational Bayesian model for multiple-person tracking is proposed. This yields a variational expectation-maximization (VEM) algorithm. The computational efficiency of the proposed method is due to closed-form expressions for both the posterior distributions of the latent variables and for the estimation of the model parameters. A stochastic process that handles person birth and person death enables the tracker to handle a varying number of persons over long periods of time. The proposed method is benchmarked using the MOT 2016 dataset

On the Stability and the Approximation of Branching Distribution Flows, with Applications to Nonlinear Multiple Target Filtering

We analyse the exponential stability properties of a class of measure-valued equations arising in nonlinear multi-target filtering problems. We also prove the uniform convergence properties w.r.t. the time parameter of a rather general class of stochastic filtering algorithms, including sequential Monte Carlo type models and mean eld particle interpretation models. We illustrate these results in the context of the Bernoulli and the Probability Hypothesis Density filter, yielding what seems to be the first results of this kind in this subject

The Cardinality Balanced Multi-Target Multi-Bernoulli Filter and Its Implementations

It is shown analytically that the multi-target multi- Bernoulli (MeMBer) recursion, proposed by Mahler, has a significant bias in the number of targets. To reduce the cardinality bias, a novel multi-Bernoulli approximation to the multi-target Bayes recursion is derived. Under the same assumptions as the MeMBer recursion, the proposed recursion is unbiased. In addition, a sequential Monte Carlo (SMC) implementation (for generic models) and a Gaussian mixture (GM) implementation (for linear Gaussian models) are proposed. The latter is also extended to accommodate mildly nonlinear models by linearization and the unscented transform

Enhanced particle PHD filtering for multiple human tracking

PhD ThesisVideo-based single human tracking has found wide application but multiple human tracking is more challenging and enhanced processing techniques are required to estimate the positions and number of targets in each frame. In this thesis, the particle probability hypothesis density (PHD) lter is therefore the focus due to its ability to estimate both localization and cardinality information related to multiple human targets. To improve the tracking performance of the particle PHD lter, a number of enhancements are proposed. The Student's-t distribution is employed within the state and measurement models of the PHD lter to replace the Gaussian distribution because of its heavier tails, and thereby better predict particles with larger amplitudes. Moreover, the variational Bayesian approach is utilized to estimate the relationship between the measurement noise covariance matrix and the state model, and a joint multi-dimensioned Student's-t distribution is exploited. In order to obtain more observable measurements, a backward retrodiction step is employed to increase the measurement set, building upon the concept of a smoothing algorithm. To make further improvement, an adaptive step is used to combine the forward ltering and backward retrodiction ltering operations through the similarities of measurements achieved over discrete time. As such, the errors in the delayed measurements generated by false alarms and environment noise are avoided. In the nal work, information describing human behaviour is employed iv Abstract v to aid particle sampling in the prediction step of the particle PHD lter, which is captured in a social force model. A novel social force model is proposed based on the exponential function. Furthermore, a Markov Chain Monte Carlo (MCMC) step is utilized to resample the predicted particles, and the acceptance ratio is calculated by the results from the social force model to achieve more robust prediction. Then, a one class support vector machine (OCSVM) is applied in the measurement model of the PHD lter, trained on human features, to mitigate noise from the environment and to achieve better tracking performance. The proposed improvements of the particle PHD lters are evaluated with benchmark datasets such as the CAVIAR, PETS2009 and TUD datasets and assessed with quantitative and global evaluation measures, and are compared with state-of-the-art techniques to con rm the improvement of multiple human tracking performance

Robust Multi-target Tracking with Bootstrapped-GLMB Filter

This dissertation presents novel multi-target tracking algorithms that obviate the need for prior knowledge of system parameters such as clutter rate, detection probabilities, and birth models. Information on these parameters is unknown but important to tracking performance. The proposed algorithms exploit the advantages of existing RFS trackers and filters by bootstrapping them. This configuration inherits the efficiency of tracking target trajectories from the RFS trackers and low complexity in parameter estimation from the RFS filters

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