To Coalesce or to Repel? An Analysis of MHT, JPDA, and Belief Propagation Multitarget Tracking Methods

Joint probabilistic data association (JPDA) filter methods and multiple hypothesis tracking (MHT) methods are widely used for multitarget tracking (MTT). However, they are known to exhibit undesirable behavior in tracking scenarios with targets in close proximity: JPDA filter methods suffer from the track coalescence effect, i.e., the estimated tracks of targets in close proximity tend to merge and can become indistinguishable, and MHT methods suffer from an opposite effect known as track repulsion. In this paper, we review the JPDA filter and MHT methods and discuss the track coalescence and track repulsion effects. We also consider a more recent methodology for MTT that is based on the belief propagation (BP) algorithm, and we argue that BP-based MTT exhibits significantly reduced track coalescence and no track repulsion. Our theoretical arguments are confirmed by numerical results.Comment: 13 page

Objective comparison of particle tracking methods

Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers

Détection et classification automatique de signaux acoustiques de baleines à bec

Les baleines à bec sont difficiles à observer et font partie des espèces les plus sensibles au bruit anthropique. L’acoustique passive est donc un outil privilégié pour étudier ces espèces et minimiser l’impact du bruit. Cet article présente une méthode de reconnaissance automatique de signaux de baleines à bec, qui se décompose en trois étapes : la détection de transitoires, la classification individuelle d’un clic, et enfin l’association de clics en trains de clics, grâce à un tracker. L’association en trains de clics permet de renforcer la classification car un clic n’est pas émis seul. De plus les trains de clics ont des caractéristiques qui peuvent être typiques de l’espèce (l’intervalle entre les clics par exemple). Les résultats sur trois espèces de baleines à bec sont présentés : le mésoplodon de Blainville, la baleine à bec de Cuvier et le mésoplodon de Gervais. Les résultats obtenus sont très encourageants.Beaked whale are difficult to observe visually and are among the most sensitive species to anthropogenic noise. Thus passive acoustic monitoring is particularly interesting to study these species and mitigate noise impact. This paper presents the outline of an automatic recognition method of beaked whale signals. This method has three steps: a transient detector, individual click classification and click association in click trains using a multihypothesis tracker. Click train association enhances classification because a click is not emitted alone and click trains have properties that can be characteristic of a species (e.g. inter-click-interval). The results are presented for three species: Blainville’s, Cuvier’s and Gervais beaked whale. The results obtained are very encouraging perpendicular to the filament) was measured. The sound level of the source as a function of energy, duration, and wavelength of the laser pulse was also measured

Advances in Active Sonar Tracking

Publication in the conference proceedings of EUSIPCO, Florence, Italy, 200

Robust Control of Markov Decision Processes and Connection to Risk-Sensitive Control

This paper introduces a formulation of the robust control problem in the partially observed Markov Decision Process (POMDP) setting. We show that this formulation is the large risk limit of the risk-sensitive control problem. Exploiting this connection, we derive an information state process and dynamic programming equations for the value function. We develop a methodology to determine an optimal policy for finite horizon problems, and near-optimal policies on the infinite horizon. Finally, we introduce an alternative formulation of the robust control problem, leading to stationary policies on the infinite horizon, and provide a methodology to determine optimal policies in this setting. 1 Introduction Robust control theory has been developed primarily in the linear systems context. It is essentially a minmax approach to control system design, whereby we choose a control to minimize the cost associated with worst-case disturbances. More recently, formulations in the nonlinear setting h..

Risk-Sensitive Control of Markov Decision Processes

This paper introduces an algorithm to determine near-optimal control laws for Markov Decision Processes with a risk-sensitive criterion. Both the fully observed and the partially observed settings are considered, for finite and infinite horizon formulations. Dynamic programming equations are introduced which characterize the value function for the partially observed, infinite horizon, discounted costs formulation. An alternative risk-sensitive formulation is examined, for which there exists a stationary infinite horizon optimal policy. Policy and value iteration algorithms are used to determine such a policy. Finally, the alternative formulation is extended in a natural way to the partially observed setting. 1 Introduction Risk-sensitive control is an area of continuing interest in stochastic control theory. It is a generalization of the classical, risk-neutral approach, whereby we seek to minimize an expression that depends not only on the total expected cost, but on h..

Risk-Sensitive, Minimax, and Mixed Risk-Neutral/Minimax Control of Markov Decision Processes

This paper analyzes a connection between risk-sensitive and minimax criteria for discrete-time, finite-state Markov Decision Processes (MDPs). We synthesize optimal policies with respect to both criteria, both for finite horizon and discounted infinite horizon problems. A generalized decision-making framework is introduced, leading to stationary risk-sensitive and minimax optimal policies on the infinite horizon with discounted costs. We introduce the mixed risk-neutral/minimax objective, and utilize results from risk-neutral and minimax control to derive an information state process and dynamic programming equations for the value function. We synthesize optimal control laws both on the finite and infinite horizon, and establish the effectiveness of the controller as a tool to trade off risk-neutral and minimax objectives