32 research outputs found
Online Audio-Visual Multi-Source Tracking and Separation: A Labeled Random Finite Set Approach
The dissertation proposes an online solution for separating an unknown and time-varying number of moving sources using audio and visual data. The random finite set framework is used for the modeling and fusion of audio and visual data. This enables an online tracking algorithm to estimate the source positions and identities for each time point. With this information, a set of beamformers can be designed to separate each desired source and suppress the interfering sources
Extended Object Tracking: Introduction, Overview and Applications
This article provides an elaborate overview of current research in extended
object tracking. We provide a clear definition of the extended object tracking
problem and discuss its delimitation to other types of object tracking. Next,
different aspects of extended object modelling are extensively discussed.
Subsequently, we give a tutorial introduction to two basic and well used
extended object tracking approaches - the random matrix approach and the Kalman
filter-based approach for star-convex shapes. The next part treats the tracking
of multiple extended objects and elaborates how the large number of feasible
association hypotheses can be tackled using both Random Finite Set (RFS) and
Non-RFS multi-object trackers. The article concludes with a summary of current
applications, where four example applications involving camera, X-band radar,
light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are
highlighted.Comment: 30 pages, 19 figure
Label Space Partition Selection for Multi-Object Tracking Using Two-Layer Partitioning
Estimating the trajectories of multi-objects poses a significant challenge
due to data association ambiguity, which leads to a substantial increase in
computational requirements. To address such problems, a divide-and-conquer
manner has been employed with parallel computation. In this strategy,
distinguished objects that have unique labels are grouped based on their
statistical dependencies, the intersection of predicted measurements. Several
geometry approaches have been used for label grouping since finding all
intersected label pairs is clearly infeasible for large-scale tracking
problems. This paper proposes an efficient implementation of label grouping for
label-partitioned generalized labeled multi-Bernoulli filter framework using a
secondary partitioning technique. This allows for parallel computation in the
label graph indexing step, avoiding generating and eliminating duplicate
comparisons. Additionally, we compare the performance of the proposed technique
with several efficient spatial searching algorithms. The results demonstrate
the superior performance of the proposed approach on large-scale data sets,
enabling scalable trajectory estimation.Comment: 6 pages, 4 figure
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