Moving Area Filter to Detect Object in Video Sequence from Moving Platform
Detecting object in video sequence is a challenging
mission for identifying, tracking moving objects. Background
removal considered as a basic step in detected moving objects tasks.
Dual static cameras placed in front and rear moving platform
gathered information which is used to detect objects. Background
change regarding with speed and direction moving platform, so
moving objects distinguished become complicated. In this paper, we
propose framework allows detection moving object with variety of
speed and direction dynamically. Object detection technique built on
two levels the first level apply background removal and edge
detection to generate moving areas. The second level apply Moving
Areas Filter (MAF) then calculate Correlation Score (CS) for
adjusted moving area. Merging moving areas with closer CS and
marked as moving object. Experiment result is prepared on real scene
acquired by dual static cameras without overlap in sense. Results
showing accuracy in detecting objects compared with optical flow
and Mixture Module Gaussian (MMG), Accurate ratio produced to
measure accurate detection moving object.
<p>[1] Karasulu,” Review And Evaluation Of Well-Known Methods For
Moving Object Detection And Tracking In Videos ”,Engineering
Department, Izmir , Journal Of Aeronautics And Space Technologies ,
Volume 4 Number 4 (11-22), July 2010.
[2] Qing-Zhong Li, Dong-Xiao He And Bing Wang, “Effective Moving
Objects Detection Based On Clustering Background Model For Video
Surveillance,” Proceedings Of The 2008 Ieee International Conference
On Image And Signal Processing - Cisp, Pp. 656-660, 27-30 May, 2008.
[3] Yuan Chen, Shengsheng Yu, Weiping Sun And Hongxing Li, “Objects
Detecting Based On Adaptive Background Models And Multiple
Cues,”Proceedings Of The 2008 Ieee International Conference On
Computing,Communication, Control, And Management - Isecs, Pp. 285-
289, 3-4 Aug, 2008.B. Smith, “An approach to graphs of linear forms
(Unpublished work style),” unpublished.
[4] Di Fan, Maoyong Cao And Changzhi Lv, “An Updating Method Of
Selfadaptive Background For Moving Objects Detection In
Video,”Proceedings Of The 2008 Ieee International Conference On
Audio,Language And Image Processing - Icalip, Pp. 1497-1501,7-9
July,2008.
[5] Bwo-Chau Fu.Ming-Yu.Shib,”Moving Object Detection Apparatus And
Method By Using Optical Flow Analysis”,Publication Number
Us8189051 B2,Publication Type Grant,Filing Date May,29,2012
[6] Clemens Rabe, Uwe Franke, And Stefan Gehrig,”Fast Detection Of
Moving Objects In Complex Scenarios”, Daimlerchrysler Ag,70546
Stuttgart, Germany ,2006..
[7] Barron J, Fleet D, Beauchemin S. “Performance Of Optical Flow
Techniques”, Int J Comp Vision ; 12(1): 43-77, 1994.M. Young, The
Techincal Writers Handbook. Mill Valley, CA: University Science,
1989.
[8] Cavallaro A, Ebrahimi T. “Classification Of Change Detection
Algorithms For Object-Based Applications”, Proc. Of Workshop On
Image Analysis For Multimedia Interactive Services (Wiamis-2003),
London (Uk), April 2003.
[9] Kentaro T, John K, Barry B, Brian M., “Wallflower: Principles And
Practice Of Background Maintenance”, Iccv, Seventh Int. Conf. On Com
Vision (Iccv'99) - 1: 255-261.1999.
[10] Javed O., Shafique K., Shah M. ,”A Hierarchical Approach To Robust
Background Subtraction Using Color And Gradient Information
Motion”,Workshop On Motion And Video Computing (Motion' 02),2002;
22-27.
[11] Cristani M., Bicego M., Murino V.,”Multi-Level Background
Initialization Using Hidden Markov Models”, In First Acm Sigmm
Int.Workshop On Video Surveillance, , 2003; 11-20.
[12] Xinyue Zhao A, Yutakasatoh B, Hidenoritakauji A, Shun’ichikaneko A,
Kenjiiwata B, Ryushiozaki C A ,” Object Detection Based On A Robust
And Accurate Statistical Multi-Point-Pair Model”, Japan, Pattern
Recognition 44- 1296–1311, Contents Listsavailableat Sciencedirect
Journalhomepage: Www.Elsevier.Com/Locate/Pr Pattern Recognition,
2011.
[13] T. Hirai, K. Sasakawa, S. Kuroda And S. Ikebata, “Detection Of Small
Moving Object By Optical Flow,” Proceedings Of The 11th Ieee
International Conference On Pattern Recognition Methodology And
Systems, Pp. 474-478, August 30-September 3, 1992.
[14] James Little, Heinrich Bulthoff And Tomaso Poggio, “Parallel Optical
Flow Computation”, Massachusetts Institute Of Technology Artificial
Intelligence Laboratory,1987.</p>
<p>[1] Karasulu,” Review And Evaluation Of Well-Known Methods For
Moving Object Detection And Tracking In Videos ”,Engineering
Department, Izmir , Journal Of Aeronautics And Space Technologies ,
Volume 4 Number 4 (11-22), July 2010.
[2] Qing-Zhong Li, Dong-Xiao He And Bing Wang, “Effective Moving
Objects Detection Based On Clustering Background Model For Video
Surveillance,” Proceedings Of The 2008 Ieee International Conference
On Image And Signal Processing - Cisp, Pp. 656-660, 27-30 May, 2008.
[3] Yuan Chen, Shengsheng Yu, Weiping Sun And Hongxing Li, “Objects
Detecting Based On Adaptive Background Models And Multiple
Cues,”Proceedings Of The 2008 Ieee International Conference On
Computing,Communication, Control, And Management - Isecs, Pp. 285-
289, 3-4 Aug, 2008.B. Smith, “An approach to graphs of linear forms
(Unpublished work style),” unpublished.
[4] Di Fan, Maoyong Cao And Changzhi Lv, “An Updating Method Of
Selfadaptive Background For Moving Objects Detection In
Video,”Proceedings Of The 2008 Ieee International Conference On
Audio,Language And Image Processing - Icalip, Pp. 1497-1501,7-9
July,2008.
[5] Bwo-Chau Fu.Ming-Yu.Shib,”Moving Object Detection Apparatus And
Method By Using Optical Flow Analysis”,Publication Number
Us8189051 B2,Publication Type Grant,Filing Date May,29,2012
[6] Clemens Rabe, Uwe Franke, And Stefan Gehrig,”Fast Detection Of
Moving Objects In Complex Scenarios”, Daimlerchrysler Ag,70546
Stuttgart, Germany ,2006..
[7] Barron J, Fleet D, Beauchemin S. “Performance Of Optical Flow
Techniques”, Int J Comp Vision ; 12(1): 43-77, 1994.M. Young, The
Techincal Writers Handbook. Mill Valley, CA: University Science,
1989.
[8] Cavallaro A, Ebrahimi T. “Classification Of Change Detection
Algorithms For Object-Based Applications”, Proc. Of Workshop On
Image Analysis For Multimedia Interactive Services (Wiamis-2003),
London (Uk), April 2003.
[9] Kentaro T, John K, Barry B, Brian M., “Wallflower: Principles And
Practice Of Background Maintenance”, Iccv, Seventh Int. Conf. On Com
Vision (Iccv'99) - 1: 255-261.1999.
[10] Javed O., Shafique K., Shah M. ,”A Hierarchical Approach To Robust
Background Subtraction Using Color And Gradient Information
Motion”,Workshop On Motion And Video Computing (Motion' 02),2002;
22-27.
[11] Cristani M., Bicego M., Murino V.,”Multi-Level Background
Initialization Using Hidden Markov Models”, In First Acm Sigmm
Int.Workshop On Video Surveillance, , 2003; 11-20.
[12] Xinyue Zhao A, Yutakasatoh B, Hidenoritakauji A, Shun’ichikaneko A,
Kenjiiwata B, Ryushiozaki C A ,” Object Detection Based On A Robust
And Accurate Statistical Multi-Point-Pair Model”, Japan, Pattern
Recognition 44- 1296–1311, Contents Listsavailableat Sciencedirect
Journalhomepage: Www.Elsevier.Com/Locate/Pr Pattern Recognition,
2011.
[13] T. Hirai, K. Sasakawa, S. Kuroda And S. Ikebata, “Detection Of Small
Moving Object By Optical Flow,” Proceedings Of The 11th Ieee
International Conference On Pattern Recognition Methodology And
Systems, Pp. 474-478, August 30-September 3, 1992.
[14] James Little, Heinrich Bulthoff And Tomaso Poggio, “Parallel Optical
Flow Computation”, Massachusetts Institute Of Technology Artificial
Intelligence Laboratory,1987.</p>
@article{"International Journal of Information, Control and Computer Sciences:50358", author = "Sallama Athab and Hala Bahjat", title = "Moving Area Filter to Detect Object in Video Sequence from Moving Platform", abstract = "Detecting object in video sequence is a challenging
mission for identifying, tracking moving objects. Background
removal considered as a basic step in detected moving objects tasks.
Dual static cameras placed in front and rear moving platform
gathered information which is used to detect objects. Background
change regarding with speed and direction moving platform, so
moving objects distinguished become complicated. In this paper, we
propose framework allows detection moving object with variety of
speed and direction dynamically. Object detection technique built on
two levels the first level apply background removal and edge
detection to generate moving areas. The second level apply Moving
Areas Filter (MAF) then calculate Correlation Score (CS) for
adjusted moving area. Merging moving areas with closer CS and
marked as moving object. Experiment result is prepared on real scene
acquired by dual static cameras without overlap in sense. Results
showing accuracy in detecting objects compared with optical flow
and Mixture Module Gaussian (MMG), Accurate ratio produced to
measure accurate detection moving object.
", keywords = "Background Removal, Correlation, Mixture Module
Gaussian, Moving Platform, Object Detection.", volume = "7", number = "9", pages = "1178-5", }
