345,519 research outputs found
DyGLIP: A Dynamic Graph Model with Link Prediction for Accurate Multi-Camera Multiple Object Tracking
Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer
vision problem due to its emerging applicability in several real-world
applications. Despite a large number of existing works, solving the data
association problem in any MC-MOT pipeline is arguably one of the most
challenging tasks. Developing a robust MC-MOT system, however, is still highly
challenging due to many practical issues such as inconsistent lighting
conditions, varying object movement patterns, or the trajectory occlusions of
the objects between the cameras. To address these problems, this work,
therefore, proposes a new Dynamic Graph Model with Link Prediction (DyGLIP)
approach to solve the data association task. Compared to existing methods, our
new model offers several advantages, including better feature representations
and the ability to recover from lost tracks during camera transitions.
Moreover, our model works gracefully regardless of the overlapping ratios
between the cameras. Experimental results show that we outperform existing
MC-MOT algorithms by a large margin on several practical datasets. Notably, our
model works favorably on online settings but can be extended to an incremental
approach for large-scale datasets.Comment: accepted at CVPR 202
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Robust improper maximum likelihood: tuning, computation, and a comparison with other methods for robust Gaussian clustering
The two main topics of this paper are the introduction of the "optimally
tuned improper maximum likelihood estimator" (OTRIMLE) for robust clustering
based on the multivariate Gaussian model for clusters, and a comprehensive
simulation study comparing the OTRIMLE to Maximum Likelihood in Gaussian
mixtures with and without noise component, mixtures of t-distributions, and the
TCLUST approach for trimmed clustering. The OTRIMLE uses an improper constant
density for modelling outliers and noise. This can be chosen optimally so that
the non-noise part of the data looks as close to a Gaussian mixture as
possible. Some deviation from Gaussianity can be traded in for lowering the
estimated noise proportion. Covariance matrix constraints and computation of
the OTRIMLE are also treated. In the simulation study, all methods are
confronted with setups in which their model assumptions are not exactly
fulfilled, and in order to evaluate the experiments in a standardized way by
misclassification rates, a new model-based definition of "true clusters" is
introduced that deviates from the usual identification of mixture components
with clusters. In the study, every method turns out to be superior for one or
more setups, but the OTRIMLE achieves the most satisfactory overall
performance. The methods are also applied to two real datasets, one without and
one with known "true" clusters
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