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research-article

A two-stage dynamic model for visual tracking

Authors:

Stanislav Kovačič,

Aleš Leonardis,

Janez PeršAuthors Info & Claims

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Volume 40, Issue 6

Pages 1505 - 1520

https://doi.org/10.1109/TSMCB.2010.2041662

Published: 01 December 2010 Publication History

Abstract

We propose a new dynamic model which can be used within blob trackers to track the target's center of gravity. A strong point of the model is that it is designed to track a variety of motions which are usually encountered in applications such as pedestrian tracking, hand tracking, and sports. We call the dynamic model a two-stage dynamic model due to its particular structure, which is a composition of two models: a liberal model and a conservative model. The liberal model allows larger perturbations in the target's dynamics and is able to account for motions in between the random-walk dynamics and the nearly constant-velocity dynamics. On the other hand, the conservative model assumes smaller perturbations and is used to further constrain the liberal model to the target's current dynamics. We implement the two-stage dynamic model in a two-stage probabilistic tracker based on the particle filter and apply it to two separate examples of blob tracking: 1) tracking entire persons and 2) tracking of a person's hands. Experiments show that, in comparison to the widely used models, the proposed two-stage dynamic model allows tracking with smaller number of particles in the particle filter (e.g., 25 particles), while achieving smaller errors in the state estimation and a smaller failure rate. The results suggest that the improved performance comes from the model's ability to actively adapt to the target's motion during tracking.

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Cited By

Lemmer SSong JCorso JKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Crowdsourcing More Effective Initializations for Single-Target Trackers Through Automatic Re-queryingProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445181(1-13)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445181
Moghaddasi SFaraji N(2020)A hybrid algorithm based on particle filter and genetic algorithm for target trackingExpert Systems with Applications: An International Journal10.1016/j.eswa.2020.113188147:COnline publication date: 1-Jun-2020
https://dl.acm.org/doi/10.1016/j.eswa.2020.113188
Dutta AMondal ADey NSen SMoraru LHassanien A(2020)Vision Tracking: A Survey of the State-of-the-ArtSN Computer Science10.1007/s42979-019-0059-z1:1Online publication date: 11-Jan-2020
https://dl.acm.org/doi/10.1007/s42979-019-0059-z
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A two-stage dynamic model for visual tracking
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks

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Information & Contributors

Information

Published In

cover image IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics Volume 40, Issue 6

December 2010

224 pages

ISSN:1083-4419

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Copyright © 2010.

Publisher

IEEE Press

Publication History

Published: 01 December 2010

Accepted: 10 January 2010

Revised: 23 October 2009

Received: 01 July 2009

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Cited By

Lemmer SSong JCorso JKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Crowdsourcing More Effective Initializations for Single-Target Trackers Through Automatic Re-queryingProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445181(1-13)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445181
Moghaddasi SFaraji N(2020)A hybrid algorithm based on particle filter and genetic algorithm for target trackingExpert Systems with Applications: An International Journal10.1016/j.eswa.2020.113188147:COnline publication date: 1-Jun-2020
https://dl.acm.org/doi/10.1016/j.eswa.2020.113188
Dutta AMondal ADey NSen SMoraru LHassanien A(2020)Vision Tracking: A Survey of the State-of-the-ArtSN Computer Science10.1007/s42979-019-0059-z1:1Online publication date: 11-Jan-2020
https://dl.acm.org/doi/10.1007/s42979-019-0059-z
Dash PPatra D(2020)An efficient hybrid framework for visual tracking using Exponential Quantum Particle Filter and Mean Shift optimizationMultimedia Tools and Applications10.1007/s11042-020-08999-z79:29-30(21513-21537)Online publication date: 1-Aug-2020
https://dl.acm.org/doi/10.1007/s11042-020-08999-z
Kim JYu SKim DToh KLee S(2017)An adaptive local binary pattern for 3D hand trackingPattern Recognition10.1016/j.patcog.2016.07.03961:C(139-152)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1016/j.patcog.2016.07.039
Zhang HWang YLuo LLu XZhang M(2017)SIFT flow for abrupt motion tracking via adaptive samples selection with sparse representationNeurocomputing10.1016/j.neucom.2017.04.024249:C(253-265)Online publication date: 2-Aug-2017
https://dl.acm.org/doi/10.1016/j.neucom.2017.04.024
Xiao JOussalah MLv Z(2016)Performance evaluation of particle filter based visual trackingJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-16908731:5(2449-2462)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.3233/JIFS-169087
Liu RHuskić GZell A(2015)On tracking dynamic objects with long range passive UHF RFID using a mobile robotInternational Journal of Distributed Sensor Networks10.1155/2015/7813802015(69-69)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1155/2015/781380
Gao MLi LSun XLuo D(2015)Face tracking based on differential harmony searchIET Computer Vision10.1049/iet-cvi.2014.00359:1(98-109)Online publication date: 1-Feb-2015
https://dl.acm.org/doi/10.1049/iet-cvi.2014.0035
Barranco FDíAz JPino BRos E(2012)A multi-resolution approach for massively-parallel hardware-friendly optical flow estimationJournal of Visual Communication and Image Representation10.1016/j.jvcir.2012.09.00423:8(1272-1283)Online publication date: 1-Nov-2012
https://dl.acm.org/doi/10.1016/j.jvcir.2012.09.004

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