Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Extended Object Tracking and Shape Classification
Date
2018-07-10
Author
Tuncer, Barkın
Kumru, Murat
Alatan, Abdullah Aydın
Özkan, Emre
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
276
views
0
downloads
Cite This
Recent extended target tracking algorithms provide reliable shape estimates while tracking objects. The estimated extent of the objects can also be used for online classification. In this work, we propose to use a Bayesian classifier to identify different objects based on their contour estimates during tracking. The proposed method uses the uncertainty information provided by the estimation covariance of the tracker.
Subject Keywords
Learning
,
Classification
,
Bayesian
,
Gaussian process
,
Shape-based classification
,
Contour representation
,
Extended target tracking
URI
https://hdl.handle.net/11511/36431
DOI
https://doi.org/10.23919/icif.2018.8455464
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
Extended Target Tracking and Classification Using Neural Networks
Tuncer, Barkın; Kumru, Murat; Özkan, Emre (2019-01-01)
Extended target/object tracking (ETT) problem involves tracking objects which potentially generate multiple measurements at a single sensor scan. State-of-the-art ETT algorithms can efficiently exploit the available information in these measurements such that they can track the dynamic behaviour of objects and learn their shapes simultaneously. Once the shape estimate of an object is formed, it can naturally be utilized by high-level tasks such as classification of the object type. In this work, we propose ...
Extended target tracking using reduced rank gaussian processes
Özcan , Mustafa Buğra; Özkan, Emre; Department of Electrical and Electronics Engineering (2021-2-12)
Conventional tracking algorithms are predominantly based on point target assumption; however, this assumption is challenged as a result of the advents in sensor resolutions. Improvements on processors and rapid advances in sensor capabilities has enabled to the perception of target characteristics beyond the kinematics. Extended target tracking is the ability to learn target shapes that occupy multiple resolution cells and to track the motion of the target in a recursive framework. Gaussian process, a non-p...
Extended Target Tracking Using Gaussian Processes
Wahlström, Niklas; Özkan, Emre (2015-08-15)
In this paper, we propose using Gaussian processes to track an extended object or group of objects, that generates multiple measurements at each scan. The shape and the kinematics of the object are simultaneously estimated, and the shape is learned online via a Gaussian process. The proposed algorithm is capable of tracking different objects with different shapes within the same surveillance region. The shape of the object is expressed analytically, with well-defined confidence intervals, which can be used ...
Fully-Automatic Target Detection and Tracking for Real-Time, Airborne Imaging Applications
Alkanat, Tunc; Tunali, Emre; Oz, Sinan (2015-03-14)
In this study, an efficient, robust algorithm for automatic target detection and tracking is introduced. Procedure starts with a detection phase. Proposed method uses two alternatives for the detection phase, namely maximally stable extremal regions detector and Canny edge detector. After detection, regions of interest are evaluated and eliminated according to their compactness and effective saliency. The detection process is repeated for a predetermined number of pyramid levels where each level processes a...
A new neural network approach to the target tracking problem with smart structure
Caylar, Selcuk; Leblebicioğlu, Mehmet Kemal; Dural, Gülbin (2006-12-01)
The algorithm presented in this paper, namely the modified neural multiple source tracking algorithm (MN-MUST) is the modified form of the recently published work, a NN algorithm, the neural multiple-source tracking (N-MUST) algorithm, was presented for locating and tracking angles of arrival from multiple sources. MN-MUST algorithm consists of three stages that are classified as the detection, filtering and DoA estimation stages. In the first stage a number of radial basis function neural networks (RBFNN) ...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
B. Tuncer, M. Kumru, A. A. Alatan, and E. Özkan, “Extended Object Tracking and Shape Classification,” 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36431.