MCMC Data Association and Sparse Factorization Updating for Real Time Multitarget Tracking with Merged and Multiple Measurements

Show full item record

Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/38685

Title: MCMC Data Association and Sparse Factorization Updating for Real Time Multitarget Tracking with Merged and Multiple Measurements
Author: Khan, Zia ; Balch, Tucker ; Dellaert, Frank
Abstract: In several multitarget tracking applications, a target may return more than one measurement per target and interacting targets may return multiple merged measurements between targets. Existing algorithms for tracking and data association, initially applied to radar tracking, do not adequately address these types of measurements. Here, we introduce a probabilistic model for interacting targets that addresses both types of measurements simultaneously. We provide an algorithm for approximate inference in this model using a Markov chain Monte Carlo (MCMC)-based auxiliary variable particle filter. We Rao-Blackwellize the Markov chain to eliminate sampling over the continuous state space of the targets. A major contribution of this work is the use of sparse least squares updating and downdating techniques, which significantly reduce the computational cost per iteration of the Markov chain. Also, when combined with a simple heuristic, they enable the algorithm to correctly focus computation on interacting targets. We include experimental results on a challenging simulation sequence. We test the accuracy of the algorithm using two sensor modalities, video, and laser range data. We also show the algorithm exhibits real time performance on a conventional PC.
Description: ©2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. DOI: 10.1109/TPAMI.2006.247
Type: Article
URI: http://hdl.handle.net/1853/38685
ISSN: 0162-8828
Citation: Khan, Z., Balch, T., & Dellaert, F. (2006). "MCMC Data Association and Sparse Factorization Updating for Real Time Multitarget Tracking with Merged and Multiple Measurements”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, no. 12, (December 2006), pp. 1960-1972.
Date: 2006-12
Contributor: Georgia Institute of Technology. Center for Robotics and Intelligent Machines
Georgia Institute of Technology. College of Computing
Publisher: Georgia Institute of Technology
Institute of Electrical and Electronics Engineers
Subject: Downdating
Laser range scanner
Linear least squares
Markov chain Monte Carlo
Merged measurements
Multitarget tracking
Particle filter
QR factorization
Rao-Blackwellized
Updatin

All materials in SMARTech are protected under U.S. Copyright Law and all rights are reserved, unless otherwise specifically indicated on or in the materials.

Files in this item

Files Size Format View
Khan06pami.pdf 1.764Mb PDF View/ Open

This item appears in the following Collection(s)

Show full item record