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Robust method of detecting moving objects in videos evolved by genetic programming

Authors:

Andy Song,

Danny FangAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 1649 - 1656

https://doi.org/10.1145/1389095.1389405

Published: 12 July 2008 Publication History

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Abstract

In this paper we investigated the use of Genetic Programming (GP) to evolve programs which could detect moving objects in videos. Two main approaches under the paradigm were proposed and investigated, single-frame approach and multi-frame approach. The former is based on analyzing individual video frames and treat them independently while the latter approach consider a sequence of frames. In the single-frame approach, three methods are investigated including using pixel intensity, pixel hue value and feature values. The experiments on Robosoccer field show that GP could detect the target under different lighting conditions and could even handle arbitrary camera positions. Although there was no domain knowledge had been provided during evolution, GP was able to produce moving object detectors that were robust and fast.

References

[1]

C. Amoroso, A. Chella, V. Morreale, P. and Storniolo. A Segmentation System for Soccer Robot Based on Neural Networks. Proc. of the RoboCup-99 Workshop, Stockholm, 1999.

Digital Library

Google Scholar

[2]

T. Bandlow, M. Klupsch, R. Hanek, T. Schmitt. Fast Image Segmentation, Object Recognition and Localization in a RoboCup Scenario, RoboCup-99: Robot Soccer World Cup III, pages 174--185, 2000.

Digital Library

Google Scholar

[3]

J. Brusey and L. Padgham. Techniques for obtaining robust, real-time, colour-based vision for robotics, Robocup Workshop, Stockholm, 1999.

Digital Library

Google Scholar

[4]

T. Burgess and V. Ciesielski. A comparison of visual object detection methods for underground mining vehicles, Commercial in Confidence, 1993.

Google Scholar

[5]

T. Dahm, S. Deutsch, M. Hebbel and Osterhues, A. Robust color classification for robot soccer. 7th International Workshop on RoboCup, 2003.

Google Scholar

[6]

J. Gutmann, T. Weigel, B. Nebel. Fast, accurate, and robust self-localization in polygonal environments. Proceedings of 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems,1999.

Google Scholar

[7]

R. Hanek, T. Schmitt, S. Buck, and M. Beetz. Fast image-based object localization in natural scenes. 2002 IEEE/RSJ International Conference on Intelligent Robots and System, 2002.

Crossref

Google Scholar

[8]

H. Kitano, M. Asada, Y. Kuniyoshi, I. Noda, E. Osawa and H. Matsubara. RoboCup: A Challenge Problem for AI and Robotics, Lecture Notes In Computer Science, pages 1--19, 1998.

Digital Library

Google Scholar

[9]

J. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Bradford Books, 1992.

Digital Library

Google Scholar

[10]

W. Smart and M. Zhang. Tracking Object Positions in Real-time Video using Genetic Programming. In Proceeding of Image and Vision Computing International Conference, pages 113--118, Akaroa, New Zealand, Nov, 2004.

Google Scholar

[11]

J. Winkeler, and B. Manjunath. Genetic programming for object detection. Genetic Programming, pages 330--335, 1997.

Google Scholar

[12]

G. Wyeth and B. Brown. Robust Adaptive Vision for Robot Soccer. Mechatronics and Machine Vision in Practice, pages 41--48, 2000.

Google Scholar

[13]

M. Zhang. Improving Object Detection Performance with Genetic Programming. International Journal on Artificial Intelligence Tools, 16(5):849--873, 2007.

Crossref

Google Scholar

[14]

M. Zhang, V. Ciesielski, and P. Andreae. A Domain-Independent Window Approach to Multiclass Object Detection Using Genetic Programming. EURASIP Journal on Applied Signal Processing, 2003(8):841--859, 2003.

Digital Library

Google Scholar

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Song AShi QYin W(2018)Understanding of GP-Evolved Motion DetectorsIEEE Computational Intelligence Magazine10.1109/MCI.2012.22285948:1(46-55)Online publication date: 17-Dec-2018
https://dl.acm.org/doi/10.1109/MCI.2012.2228594
Mi ZShang LXue B(2018)Multi-Dimensional Optical Flow Embedded Genetic Programming for Anomaly Detection in Crowded ScenesNeural Information Processing10.1007/978-3-030-04179-3_43(486-497)Online publication date: 18-Nov-2018
https://doi.org/10.1007/978-3-030-04179-3_43
Xie CShang L(2014)Anomaly detection in crowded scenes using genetic programming2014 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2014.6900396(1832-1839)Online publication date: Jul-2014
https://doi.org/10.1109/CEC.2014.6900396
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Index Terms

Robust method of detecting moving objects in videos evolved by genetic programming
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Video summarization

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Published In

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

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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Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Song AShi QYin W(2018)Understanding of GP-Evolved Motion DetectorsIEEE Computational Intelligence Magazine10.1109/MCI.2012.22285948:1(46-55)Online publication date: 17-Dec-2018
https://dl.acm.org/doi/10.1109/MCI.2012.2228594
Mi ZShang LXue B(2018)Multi-Dimensional Optical Flow Embedded Genetic Programming for Anomaly Detection in Crowded ScenesNeural Information Processing10.1007/978-3-030-04179-3_43(486-497)Online publication date: 18-Nov-2018
https://doi.org/10.1007/978-3-030-04179-3_43
Xie CShang L(2014)Anomaly detection in crowded scenes using genetic programming2014 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2014.6900396(1832-1839)Online publication date: Jul-2014
https://doi.org/10.1109/CEC.2014.6900396
Shi QWei Yin Song A(2012)Analysis of motion detectors evolved by Genetic Programming2012 IEEE Congress on Evolutionary Computation10.1109/CEC.2012.6256535(1-8)Online publication date: Jun-2012
https://doi.org/10.1109/CEC.2012.6256535
Feng Xie Song A(2012)Evolving frame splitters by Genetic Programming2012 IEEE Congress on Evolutionary Computation10.1109/CEC.2012.6256161(1-7)Online publication date: Jun-2012
https://doi.org/10.1109/CEC.2012.6256161
Shi QSong A(2011)Selective motion detection by Genetic Programming2011 IEEE Congress of Evolutionary Computation (CEC)10.1109/CEC.2011.5949659(496-503)Online publication date: Jun-2011
https://doi.org/10.1109/CEC.2011.5949659
Song AChen DZhang M(2010)Contribution based bloat control in Genetic ProgrammingIEEE Congress on Evolutionary Computation10.1109/CEC.2010.5586372(1-8)Online publication date: Jul-2010
https://doi.org/10.1109/CEC.2010.5586372
Song APinto B(2010)Study of GP representations for motion detection with unstable backgroundIEEE Congress on Evolutionary Computation10.1109/CEC.2010.5586334(1-8)Online publication date: Jul-2010
https://doi.org/10.1109/CEC.2010.5586334
Pinto BSong ARothlauf F(2009)Motion detection in complex environments by genetic programmingProceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers10.1145/1570256.1570288(2125-2130)Online publication date: 8-Jul-2009
https://dl.acm.org/doi/10.1145/1570256.1570288
Song AChen DZhang MRothlauf F(2009)Bloat control in genetic programming by evaluating contribution of nodesProceedings of the 11th Annual conference on Genetic and evolutionary computation10.1145/1569901.1570221(1893-1894)Online publication date: 8-Jul-2009
https://dl.acm.org/doi/10.1145/1569901.1570221
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Motion detection in complex environments by genetic programming

Genetic programming for detecting target motions

Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles

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