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MOVING OBJECT
DETECTION
Presentation By:
Deepak Gambhir Saurabh Sharma Manav Mittal
(ICE-III,BVCOE) (ECE-IV,BVCOE) (ICE-III,BVCOE)
CONTEXT OF PAPER
Algorithm for object detection
By calculating RGB content &
Illumination.
PLATFORMS
• MATLAB
(Digital image processing)
• Experimental Result Reports
Objective
• To find the RGB content of the moving object.
• To find the illumintaion.
Basic principle
According to physics, an object is
considered to be colorless until the light of
suitable wavelength falls on it.
The color of the object is decided by the
amount of wavelength absorbed and
reflected by the object, which states that
the “amount of light falling on an object
determines the color of the image”.
illumintaion- property of light which varies
according to the object.
It changes its value even if the object is
observed from stationary position at time
(t1) to motion position at time( t2).
This change in illumination causes its
color to change a bit thus changing the
RGB configuration which is used to get
the desired result.
EXPERIMENTAL DATA
1) Stationary object and stationary
environment
Amount of illumination
2) Stationary environment and partially
moving object
Amount of illumination
3) Stationary environment and object
moving alongwith body
Amount of illumination
Evaluation
The detection of object can be done on
the comparison of illumination and RGB
configuration.
• As the object attains a velocity, the RGB
configuration of the object changes due to
change in illumination.
• The detection of object can be done on
the basis of illumination and RGB
configuration.
Conclusions
Limitations
• Difficult to detect objects in dark.
• Slow moving objects couldn’t be detected
too.
Future Aspects
• SECURITY SYSTEM: The object to be
detected always faces by a camera and as
the object attains a velocity, the RGB
configuration of the object changes due to
change in illumination. Hardware should
be design in such a way that as the
camera changes its position an alarm gets
activated.
• The detection of lip movements so as to
design a device that can change the
movement of lips into speech recognition
so that person with the disabilities to
recognize the frequency is able to
communicate without any problem.
REFERENCES
• Reid Porter, Neil Harvey, James Theiler,”a change detection
approach to moving object detection in low frame –rate video”,
space and remote sensing sciences group.
• Jong Bae Kim, Schof Comput Eng., Seoul Digital University, Seoul
• Tao Xia1, Chaoqiang Liu2, Hui Li2,”efficient moving object detection
and description, National Singapore University
• 4)Michal Irani, P. Anandan, David Sarnoff Research Center, “a
unified approach to a moving object detection in 2D and 3D
scenes”.
• Mrs. Renuka S. Sindge Department Of IT Govt. Polytechnic College,
Mrs. Shubhangisapkal Department of Comp. Sciences Govt.
College of engineering “multiple object detection from real time
video sequence”
• J.H Park1, G.S.Lee1, W.H.Cho2, Chhonam National University,
N.Taon1, S.H..Kim1, S.Y.Park3, Mokpo National University, “moving
object based detection based on clauses entropy”.
• Fu-Yuan Hu, Yan-Ning Zhan, Lan Yao, “an effective detection
algorithm for moving object with complex background”,
Northwestern Poly-Technical University.
• Zhan Chaohui Duan Xiaohui, Xu Shuoyu Song Zheng Luo Min, “an
improved moving object detection algorithm based on frame
difference and edge detection”, Peking University.
• Victor Mejia*, Eun-Young Kang “automatic moving object detection
using motion and color features and bi-model Gaussian
approximation”. California State University
• Jiman Kimam, Guensu Ye, Daijan Kim “moving object detection
under free-moving camera”, Pohang University of Science and
Technology
• Chen Peijiang, “moving object detection based on the background
extraction”, Linyi Normal University
• Chin Chun Huang and Sheng-Jyh Wang, “a cascaded hierarchical
framework for moving bject detection”, National Chio Tung
University, Taiwan
• Ben-Hsiang do, Shih-Chia Huang, “dyamic background modeling
based on radial basis function neural networks for moving object
detection”, National Taipei University of Technology.
• Jacinto Nascimento, Jorge Marques, “performance evaluation of
object detection algorithms for video survEillence”, Member, IEEE.
THANK YOU

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Moving object detection

  • 1. MOVING OBJECT DETECTION Presentation By: Deepak Gambhir Saurabh Sharma Manav Mittal (ICE-III,BVCOE) (ECE-IV,BVCOE) (ICE-III,BVCOE)
  • 2. CONTEXT OF PAPER Algorithm for object detection By calculating RGB content & Illumination. PLATFORMS • MATLAB (Digital image processing) • Experimental Result Reports
  • 3. Objective • To find the RGB content of the moving object. • To find the illumintaion.
  • 4. Basic principle According to physics, an object is considered to be colorless until the light of suitable wavelength falls on it. The color of the object is decided by the amount of wavelength absorbed and reflected by the object, which states that the “amount of light falling on an object determines the color of the image”.
  • 5. illumintaion- property of light which varies according to the object. It changes its value even if the object is observed from stationary position at time (t1) to motion position at time( t2). This change in illumination causes its color to change a bit thus changing the RGB configuration which is used to get the desired result.
  • 7. 1) Stationary object and stationary environment
  • 9. 2) Stationary environment and partially moving object
  • 11. 3) Stationary environment and object moving alongwith body
  • 13. Evaluation The detection of object can be done on the comparison of illumination and RGB configuration.
  • 14. • As the object attains a velocity, the RGB configuration of the object changes due to change in illumination. • The detection of object can be done on the basis of illumination and RGB configuration. Conclusions
  • 15. Limitations • Difficult to detect objects in dark. • Slow moving objects couldn’t be detected too.
  • 16. Future Aspects • SECURITY SYSTEM: The object to be detected always faces by a camera and as the object attains a velocity, the RGB configuration of the object changes due to change in illumination. Hardware should be design in such a way that as the camera changes its position an alarm gets activated.
  • 17. • The detection of lip movements so as to design a device that can change the movement of lips into speech recognition so that person with the disabilities to recognize the frequency is able to communicate without any problem.
  • 18. REFERENCES • Reid Porter, Neil Harvey, James Theiler,”a change detection approach to moving object detection in low frame –rate video”, space and remote sensing sciences group. • Jong Bae Kim, Schof Comput Eng., Seoul Digital University, Seoul • Tao Xia1, Chaoqiang Liu2, Hui Li2,”efficient moving object detection and description, National Singapore University • 4)Michal Irani, P. Anandan, David Sarnoff Research Center, “a unified approach to a moving object detection in 2D and 3D scenes”. • Mrs. Renuka S. Sindge Department Of IT Govt. Polytechnic College, Mrs. Shubhangisapkal Department of Comp. Sciences Govt. College of engineering “multiple object detection from real time video sequence” • J.H Park1, G.S.Lee1, W.H.Cho2, Chhonam National University, N.Taon1, S.H..Kim1, S.Y.Park3, Mokpo National University, “moving object based detection based on clauses entropy”. • Fu-Yuan Hu, Yan-Ning Zhan, Lan Yao, “an effective detection algorithm for moving object with complex background”, Northwestern Poly-Technical University.
  • 19. • Zhan Chaohui Duan Xiaohui, Xu Shuoyu Song Zheng Luo Min, “an improved moving object detection algorithm based on frame difference and edge detection”, Peking University. • Victor Mejia*, Eun-Young Kang “automatic moving object detection using motion and color features and bi-model Gaussian approximation”. California State University • Jiman Kimam, Guensu Ye, Daijan Kim “moving object detection under free-moving camera”, Pohang University of Science and Technology • Chen Peijiang, “moving object detection based on the background extraction”, Linyi Normal University • Chin Chun Huang and Sheng-Jyh Wang, “a cascaded hierarchical framework for moving bject detection”, National Chio Tung University, Taiwan • Ben-Hsiang do, Shih-Chia Huang, “dyamic background modeling based on radial basis function neural networks for moving object detection”, National Taipei University of Technology. • Jacinto Nascimento, Jorge Marques, “performance evaluation of object detection algorithms for video survEillence”, Member, IEEE.

Editor's Notes

  1. Lang-language