BACKGROUND MODEL CONSTRUCTION AND MAINTENANCE IN A VIDEO SURVEILLANCE SYSTEM Computer Vision...

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BACKGROUND MODEL CONSTRUCTION AND MAINTENANCE IN A VIDEO SURVEILLANCE SYSTEM Computer Vision Laborator y 指指指指 指指指 指指 指指指 指指指

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Introduction(1) Background objects, foreground ones. Moving object detection, tracking, object type classification, human motion analysis, behavior understanding. Background subtraction, temporal differencing, optical flow analysis.

Transcript of BACKGROUND MODEL CONSTRUCTION AND MAINTENANCE IN A VIDEO SURVEILLANCE SYSTEM Computer Vision...

Page 1: BACKGROUND MODEL CONSTRUCTION AND MAINTENANCE IN A VIDEO SURVEILLANCE SYSTEM Computer Vision Laboratory 指導教授:張元翔 老師 研究生:許木坪.

BACKGROUND MODEL CONSTRUCTION AND MAINTENANCE IN A VIDEO SURVEILLANCE SYSTEM

Computer Vision Laboratory指導教授:張元翔 老師研究生:許木坪

Page 2: BACKGROUND MODEL CONSTRUCTION AND MAINTENANCE IN A VIDEO SURVEILLANCE SYSTEM Computer Vision Laboratory 指導教授:張元翔 老師 研究生:許木坪.

Outline

IntroductionInitial background model constructionBackground model adjustmentBackground model replacementMoving object detectionExperimental results and analysisConclusionsReferences

Page 3: BACKGROUND MODEL CONSTRUCTION AND MAINTENANCE IN A VIDEO SURVEILLANCE SYSTEM Computer Vision Laboratory 指導教授:張元翔 老師 研究生:許木坪.

Introduction(1)

Background objects, foreground ones.Moving object detection, tracking, object

type classification, human motion analysis, behavior understanding.

Background subtraction, temporal differencing, optical flow analysis.

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Introduction(2)

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Initial background model construction(1)

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Initial background model construction(2)

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Background model adjustment(1)

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Background model adjustment(2)

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Background model replacement(1)

Superimposed image analysis

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Background model replacement(2)

Candidate frames collection

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Background model replacement(3)

Background model replacement

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Moving object detection

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Experimental results and analysis

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Conclusions

Initial background model construction, sustained background model adjustment, rapid background model replacement.

Future – process an insufficient light environment during the night.object tracking, face recognition, and behavior understanding.

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References

The 18th IPPR Conference on Computer Vision, Graphics and Image Processing