ERA

Download the full-sized PDF of Artificial learning approaches for multi-target tracking.Download the full-sized PDF

Analytics

Share

Permanent link (DOI): https://doi.org/10.7939/R3SJ19V38

Download

Export to: EndNote  |  Zotero  |  Mendeley

Communities

This file is in the following communities:

Mathematical and Statistical Sciences, Department of

Collections

This file is in the following collections:

Research Publications (Mathematical and Statistical Sciences)

Artificial learning approaches for multi-target tracking. Open Access

Descriptions

Author or creator
Blount, Douglas
Kouritzin, Michael
McCrosky, Jesse
Additional contributors
Subject/Keyword
nonlinear filtering
SERP filter
stochastic process
particle filter
neural networks
artificial learning
Type of item
Conference/workshop Presentation
Language
English
Place
Time
Description
A hybrid weighted/interacting particle filter, the selectively resampling particle (SERP) filter, is used to detect and track an unknown number of independent targets on a one-dimensional \"racetrack\" domain. The targets evolve in a nonlinear manner. The observations model a sensor positioned above the racetrack. The observation data takes the form of a discretized image of the racetrack, in which each discrete segment has a value depending both upon the presence or absence of targets in the corresponding portion of the domain, and upon lognormal noise. The SERP filter provides a conditional distribution approximated by particle simulations. After each observation is processed, the SERP filter selectively resamples its particles in a pairwise fashion, based on their relative likelihood. We consider a reinforcement learning approach to control this resampling. We compare two different ways of applying the filter to the problem: the signal measure approach and the model selection approach. We present quantitative results of the ability of the filter to detect and track the targets, for each of the techniques. Comparisons are made between the signal measure and model selection approaches, and between the dynamic and static resampling control techniques.
Date created
2004
DOI
doi:10.7939/R3SJ19V38
License information
Rights
Copyright 2004 Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Citation for previous publication
D. Blount, N. Hu, M.A. Kouritzin, and J. McCrosky, "Artificial learning approaches for multi-target tracking", in Automatic Target Recognition XIV, edited by F.A. Sadjadi, 2004 Proceedings of SPIE, 5426: 293-304. doi:10.1117/12.542674
Source
Link to related item

File Details

Date Uploaded
Date Modified
2014-05-01T04:22:55.667+00:00
Audit Status
Audits have not yet been run on this file.
Characterization
File format: pdf (Portable Document Format)
Mime type: application/pdf
File size: 565459
Last modified: 2015:10:12 17:25:11-06:00
Filename: SPIE_2004_5426_293.pdf
Original checksum: b395b28cab2697f689a0ac95ae64d257
Well formed: true
Valid: true
Page count: 12
Activity of users you follow
User Activity Date