Selective Motion Estimation for Surveillance Videos

Muhammad Akram; Naeem Ramzan; Ebroul Izquierdo

User Centric Media. First International Conference, UCMedia 2009, Venice, Italy, December 9-11, 2009, Revised Selected Papers

Research Article

Selective Motion Estimation for Surveillance Videos

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@INPROCEEDINGS{10.1007/978-3-642-12630-7_23,
    author={Muhammad Akram and Naeem Ramzan and Ebroul Izquierdo},
    title={Selective Motion Estimation for Surveillance Videos},
    proceedings={User Centric Media. First International Conference, UCMedia 2009, Venice, Italy, December 9-11, 2009, Revised Selected Papers},
    proceedings_a={UCMEDIA},
    year={2012},
    month={10},
    keywords={Fast motion estimation surveillance video background subtraction block matching algorithm},
    doi={10.1007/978-3-642-12630-7_23}
}

Muhammad Akram
Naeem Ramzan
Ebroul Izquierdo
Year: 2012
Selective Motion Estimation for Surveillance Videos
UCMEDIA
Springer
DOI: 10.1007/978-3-642-12630-7_23

Muhammad Akram¹^,*, Naeem Ramzan¹^,*, Ebroul Izquierdo¹^,*

1: Queen Mary University of London

*Contact email: muhammad.akram@elec.qmul.ac.uk, naeem.ramzan@elec.qmul.ac.uk, ebroul.izquierdo@elec.qmul.ac.uk

Abstract

In this paper, we propose a novel approach to perform efficient motion estimation specific to surveillance videos. A real-time background subtractor is used to detect the presence of any motion activity in the sequence. Two approaches for selective motion estimation, GOP-by-GOP and Frame-by-Frame, are implemented. In the former, motion estimation is performed for the whole group of pictures (GOP) only when moving object is detected for any frame of the GOP. While for the latter approach; each frame is tested for the motion activity and consequently for selective motion estimation. Experimental evaluation shows that significant reduction in computational complexity can be achieved by applying the proposed strategy.

Keywords: Fast motion estimation surveillance video background subtraction block matching algorithm

Published: 2012-10-23

: http://dx.doi.org/10.1007/978-3-642-12630-7_23