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Combined shape and feature-based video analysis and its application to
non-rigid object tracking
資訊碩一 10077034 蔡勇儀2011/11/01 @LAB603
Introduction Method
Background generation and updating Detection of moving object Shape control points Combined shape and feature-based object
tracking Object occlusion
Result Conclusions
Outline
Object motion detect is an important issue of computer vision.
Many challenges Complex background More object motion Occlusion Illumination change Dynamic shading Camera jitter …
Introduction – object motion
Active shape model(ASM) Pre-model object’s shape Priori trained shape information Manually determined landmark point Can’t real time
Non-prior training active feature model(NPT-AFM) Consider feature point without object shape Improve computational efficiency Doesn’t utilise background information
Introduction – methods(1/2)
Block matching algorithm(BMA) Block matching between two frame Direct matching nature simplifies motion Preserves object’s feature which can’t
be easily parameterized
Poor performance with non-rigid shapes and similar patterns to the background.
Introduction – methods(2/2)
1. Background generation2. Motion detection and SCP extraction3. Object shape tracking modules
Introduction – This paper method
Introduction Method
Background generation and updating Detection of moving object Shape control points Combined shape and feature-based
object tracking Result Conclusions
Outline
Use median filter & BMA Define sum of absolute
difference(SAD) and threshold(0.05)
Find background(Static)
Method – Background generation
Method – Detection of moving object
Find feasible boundary
R represents the minimum rectangular box enclosing the object.
Method – Shape control points(1/2)
Build SCP set
K: interval of skipping redundant SCPs
Method – Shape control points(2/2)
Get block SCP
If object deformation, occlusion(25%)… CBMA – computing distances among
SCPs PBMA – fix motion region
Method - Combined shape and feature-based object tracking
Method - Summary
Introduction Method
Background generation and updating Detection of moving object Shape control points Combined shape and feature-based
object tracking Result Conclusions
Outline
Result(1/2)
Result(2/2)
Introduction Method
Background generation and updating Detection of moving object Shape control points Combined shape and feature-based
object tracking Result Conclusions
Outline
BMA & CBMA
The number of SCPs
Optimal region(feature histogram)
Conclusions
Source :IET Image Process, 2011, Vol.5, Iss.1, pp.87-100
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