Object triangulation, 3-D object tracking, feature correspondence, and camera calibration are key problems for estimation from camera networks. This paper addresses these problems within a unified Bayesian framework for joint multiobject tracking and sensor registration. Given that using standard filtering approaches for state estimation from cameras is problematic, an alternative parametrisation is exploited, called disparity space. The disparity space-based approach for triangulation and object tracking is shown to be more effective than non-linear versions of the Kalman filter and particle filtering for non-rectified cameras. The approach for feature correspondence is based on the Probability Hypothesis Density (PHD) filter, and hence inherits the ability to update without explicit measurement association, to initiate new targets, and to discriminate between target and clutter. The PHD filtering approach then forms the basis of a camera calibration method from static or moving objects. Results are shown on simulated data.

No abstract

Surveillance activities with ground-based assets in the context of space situational awareness are particularly challenging. The observation process is indeed hindered by short observation arcs, limited observability, missed detections, measurement noise, and contamination by clutter. This paper exploits a recent estimation framework for stochastic populations for space situational awareness surveillance scenarios. This framework shares the flexibility of the finite set statistics framework in the modeling of a dynamic population of objects and the representation of all the sources of uncertainty in a single coherent probabilistic framework and the intuitive approach of traditional trackbased techniques to describe individual objects and maintain track continuity. We present a recent multi-object filtering solution derived from this framework, the filter for distinguishable and independent stochastic populations, and propose a bespoke implementation of the multitarget tracking algorithm for a space situational awareness surveillance activity. The distinguishable and independent stochastic populations filter is tested on a surveillance scenario involving two ground-based Doppler radars in a challenging environment with significant measurement noise, limited observability, missed detections, false alarms, and no a priori knowledge about the number and the initial states of the objects in the scene. The tracking algorithm shows good performance in initiating tracks from object-generated observations and in maintaining track custody throughout the scenario, even when the objects are outside of the sensors' fields of view, despite the challenging conditions of the surveillance scenario.Nomenclature α y = probability of existence of previously detected target with observation path y cH = probability of existence of hypothesis H H = hypothesis, i.e., subset of compatible tracks in Y (H, n) = multitarget configuration, describing a possible composition of population X H tjt−1 ; H t = set of all hypotheses, propagated by the filter for distinguishable and independent stochastic populations (DISP) lz; x = likelihood of association between target with state x and observation with state z P tjt−1 , P t = law of whole population of targets, propagated by the distinguishable and independent stochastic populations filter P a = law of population of appearing targets p d x = probability of detection of target with state x p fa z = probability that observation with state z is false alarm p y = probability distribution of previously detected target with obs. path y p ϕ = probability distribution of each yet-to-be-detected target wH; n = joint probability of existence of targets in configuration (H, n) X = population of targets X = target state space X = target state space augmented with empty state ψ x = target state (e.g., position and velocity coordinates) Y tjt−1 , Y t = set of all observation paths, propagated by distinguishable and independent stochastic populations filter y = observation path or track characterized by said o...

Fluorescence microscopy is a technique which allows the imaging of cellular and intracellular dynamics through the activation of fluorescent molecules attached to them. It is a very important technique because it can be used to analyze the behavior of intracellular processes in vivo in contrast to methods like electron microscopy. There are several challenges related to the extraction of meaningful information from images acquired from optical microscopes due to the low contrast between objects and background and the fact that point-like objects are observed as blurred spots due to the diffraction limit of the optical system. Another consideration is that for the study of intracellular dynamics, multiple particles must be tracked at the same time, which is a challenging task due to problems such as the presence of false positives and missed detections in the acquired data. Additionally, the objective of the microscope is not completely static with respect to the cover slip due to mechanical vibrations or thermal expansions which introduces bias in the measurements. In this paper, a Bayesian approach is used to simultaneously track the locations of objects with different motion behaviors and the stage drift using image data obtained from fluorescence microscopy experiments. Namely, detections are extracted from the acquired frames using image processing techniques, and then these detections are used to accurately estimate the particle positions and simultaneously correct the drift introduced by the motion of the sample stage. A single cluster Probability Hypothesis Density (PHD) filter with object classification is used for the estimation of the multiple target state assuming different motion behaviors. The detection and tracking methods are tested and their performance is evaluated on both simulated and real data.

No abstract