1. 資訊碩一 10077034 蔡勇儀 2011/11/01 @LAB603

2.  Introduction  Method  Background generation and updating  Detection of moving object  Shape control points  Combined shape and feature-based object tracking  Object occlusion  Result  Conclusions

3.  Object motion detect is an important issue of computer vision.  Many challenges  Complex background  More object motion  Occlusion  Illumination change  Dynamic shading  Camera jitter  …

4.  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

5.  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.

6. 1. Background generation 2. Motion detection and SCP extraction 3. Object shape tracking modules

7.  Introduction  Method  Background generation and updating  Detection of moving object  Shape control points  Combined shape and feature-based object tracking  Result  Conclusions

8.  Use median filter & BMA  Define sum of absolute difference(SAD) and threshold(0.05)  Find background(Static)

10.  Find feasible boundary R represents the minimum rectangular box enclosing the object.

11.  Build SCP set  K: interval of skipping redundant SCPs

12.  Get block SCP  If object deformation, occlusion(25%)…  CBMA – computing distances among SCPs  PBMA – fix motion region

14.  Introduction  Method  Background generation and updating  Detection of moving object  Shape control points  Combined shape and feature-based object tracking  Result  Conclusions

17.  Introduction  Method  Background generation and updating  Detection of moving object  Shape control points  Combined shape and feature-based object tracking  Result  Conclusions

18.  BMA & CBMA  The number of SCPs  Optimal region(feature histogram)

19.  Source ： IET Image Process, 2011, Vol.5, Iss.1, pp.87-100