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A new approach for adaptive background object tracking based on Kalman filter and mean shift

Authors:

Kicheon HongAuthors Info & Claims

RACS '13: Proceedings of the 2013 Research in Adaptive and Convergent Systems

Pages 134 - 139

https://doi.org/10.1145/2513228.2513262

Published: 01 October 2013 Publication History

Abstract

Mean shift algorithm is one of popular methods to visual object tracking and has some advantages comparing to other tracking methods. Aiming at the shortcoming of the Mean shift algorithm, this paper proposed a novel object tracking approach using Kalman filter and adaptive background Mean shift. On the one hand, the combination of Kalman filter with Mean shift is suit to handle the case of target appearance drastically changing and occlusion. On the other hand, Bayes law is used to adjust the color probability distribution. It enables objects to be tracked, even when move across regions of background which are the same color as a significant portion of the object. Experimental results demonstrate that this algorithm can track the object accurately in conditions of abrupt shifts, as well as clutter and partial occlusions occurring to the tracking object with good robustness.

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Cited By

Zhang WJing YZhang YLin TYan J(2024)Retina-U: A Two-Level Real-Time Analytics Framework for UHD Live Video StreamingIEEE Transactions on Broadcasting10.1109/TBC.2023.334564670:2(429-440)Online publication date: Jun-2024
https://doi.org/10.1109/TBC.2023.3345646
胡绍(2014)Study on Real-Time Target TrackingComputer Science and Application10.12677/CSA.2014.4802304:08(158-168)Online publication date: 2014
https://doi.org/10.12677/CSA.2014.48023

Index Terms

A new approach for adaptive background object tracking based on Kalman filter and mean shift
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
      2. Computer vision tasks
        Scene understanding

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Information

Published In

cover image ACM Conferences

RACS '13: Proceedings of the 2013 Research in Adaptive and Convergent Systems

October 2013

529 pages

ISBN:9781450323482

DOI:10.1145/2513228

Conference Chairs:
Ching Y. Suen
Concordia University, Canada
,
Amir Aghdam
Concordia University, Canada
,
Minyi Guo
Shanghai Jiao Tong University, China
,
Program Chairs:
Jiman Hong
Soongsil University, Korea
,
Esmaeil Nadimi
University of Southern Denmark, Denmark

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing
ACCT: Association of Convergent Computing Technology

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 October 2013

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GRRC program of Gyeonggi Province, Korea

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RACS'13

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SIGAPP
ACCT

RACS'13: Research in Adaptive and Convergent Systems

October 1 - 4, 2013

Quebec, Montreal, Canada

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RACS '13 Paper Acceptance Rate 73 of 317 submissions, 23%;

Overall Acceptance Rate 393 of 1,581 submissions, 25%

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Cited By

Zhang WJing YZhang YLin TYan J(2024)Retina-U: A Two-Level Real-Time Analytics Framework for UHD Live Video StreamingIEEE Transactions on Broadcasting10.1109/TBC.2023.334564670:2(429-440)Online publication date: Jun-2024
https://doi.org/10.1109/TBC.2023.3345646
胡绍(2014)Study on Real-Time Target TrackingComputer Science and Application10.12677/CSA.2014.4802304:08(158-168)Online publication date: 2014
https://doi.org/10.12677/CSA.2014.48023

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