In this paper we study the problem of tracking an arbitrary number of people with multiple heterogeneous sensors. To solve the problem, we start with a Bayesian derivation of the multiple-hypothesis tracking (MHT), and, under certain assumptions, we arrive to the joint probabilistic data association filter (JPDAF). In their original derivation, both the MHT and JPDAF assume a multiple sensor sc...

Hongshe Dang Institute of Synthetic Automation Xi’an Jiaotong University Xi’an, Shaan xi 710049, P. R. China [email protected] Chongzhao Han Institute of Synthetic Automation Xi’an Jiaotong University Xi’an, Shaan xi 710049, P. R. China [email protected] Zhansheng Duan Institute of Synthetic Automation Xi’an Jiaotong University Xi’an, Shaan xi 710049, P. R. China [email protected] Ab...

Two algorithms for hypothesis generation in Multiple Target Tracking (MTT) with Joint Probabilistic Data Association Filter (JPDAF) have been developed and compared. The known Depth First Search (DFS) algorithm has been used as base approach. Some worst case examples have been used to compare effectiveness of the two program implementations in C++. The results proved that the recursive procedur...

Multitarget tracking (MTT) deals with the state estimation of an unknown number of moving targets. Available measurements may both arise from the targets if they are detected, and from clutter. Clutter is generally considered as a model describing false alarms. Its (spatio-temporal) statistical properties are quite different from those of the target, which makes the extraction of target tracks ...

The classical particle filter deals with the estimation of one state process conditioned on a realization of one observation process. We extend it here to the estimation of multiple state processes given realizations of several kinds of observation processes. The new algorithm is used to track with success multiple targets in a bearings-only context, whereas a JPDAF diverges. Making use of the ...

The tracking of targets in road situation represents a challenge for both the measurement to track association and the positional estimation algorithms. Previous simulation have shown that the data association method based on evidence theory has a good performance, compared with the Nearest Neighbor (NN) and cheap JPDAF method, moreover it has proved that the Interacting Multiple Models (IMM) m...

In prior work, we developed a speaker tracking system based on an extended Kalman filter using time delays of arrival (TDOAs) as acoustic features. While this system functioned well, its utility was limited to scenarios in which a single speaker was to be tracked. In this work, we remove this restriction by generalizing the IEKF, first to a probabilistic data association filter, which incorpora...

In this paper, a new multi-target tracking algorithm based on fuzzy logic for tracking in clutter is developed, it is called directional fuzzy data association (DFDA) filter. The new algorithm incorporates the directional information of the targets for data association with the Mahalanobis distance. Firstly, the directional information, called pseudo-direction, is defined; the method of how to ...

The Multiple Targets Tracking (MTT) problem is addressed in signal and image processing. When the state and measurement models are linear, we can find several algorithms that yield good performances in MTT problem, among them, the Multiple Hypotheses Tracker (MHT) and the Joint Probabilistic Data Association Filter (JPDAF). However, if the state and measurement models are nonlinear, these algor...

In prior work, we developed a speaker tracking system based on an extended Kalman filter using time delays of arrival (TDOAs) as acoustic features. While this system functioned well, its utility was limited to scenarios in which a single speaker was to be tracked. In this work, we remove this restriction by generalizing the IEKF, first to a probabilistic data association filter, which incorpora...