A Discriminatively Trained, Multiscale , Deformable Part Model

1. A Discriminatively Trained,Multiscale, Deformable Part Model Computer Vision and Pattern Recognition,2008 2014-05-13 Yeong-Jun Cho

2. Contents • Introduction • Part-based model • Overviewing of Training Models using Latent SVM • Results • Conclusion

3. A Discriminatively Trained, Multiscale, Deformable Part Model Introduction • Object detection and localization • Goal • Detect and localize objects from generic categories in static images • Training: bounding boxes around objects • Challenges • Illumination changes • Viewpoint • Intraclass variability • Non-rigid deformation

4. A Discriminatively Trained, Multiscale, Deformable Part Model Introduction • Object detection and localization • Idea • A collection of parts arranged ina deformable configuration • Coarse model with detailed models • Challenges • Illumination changes • Viewpoint • Intraclass variability • Non-rigid deformation Detection results using Deformable part model

5. A Discriminatively Trained, Multiscale, Deformable Part Model Part-based model • A collection of parts arranged in a deformable configuration • Part locations are not known: latent variables • Star model (1 root + multiple parts) • Parts filter at twice resolution of the root filter • Score of the detection: Root filter Part filters Deformation cost

6. A Discriminatively Trained, Multiscale, Deformable Part Model Part-based model • Simple model • Part-based model • : model parameter (e.g. w, b) • : feature vector z • : model parameter (e.g. w, b) • : feature vector • z: speciation of object configuration Score : max over components

7. A Discriminatively Trained, Multiscale, Deformable Part Model Part-based model • Build a feature pyramid H for dealing with scale • 5 levels per octave at training, 10 at testing • Filter weight vector F • Position p= (x, y, l) • Score of F at p :

8. A Discriminatively Trained, Multiscale, Deformable Part Model Part-based model • A model • A root filter • N parts • A part filter • : number of part filters • An anchor • Deformation costs • Position of root and parts

9. A Discriminatively Trained, Multiscale, Deformable Part Model Part-based model • Score of a detector • . • : possible location of part filters • : location of a root filter • : location of anchors • Deformation costs : -> coefficients of quadratic functions Deformation costs of part filters Sum of root and part filters scores x2 resolution

10. A Discriminatively Trained, Multiscale, Deformable Part Model Part-based model • Score of a detector • The score can be written as : Deformation costs of part filters Sum of root and part filters scores =

11. A Discriminatively Trained, Multiscale, Deformable Part Model Overviewing ofTraining models using Latent SVM • Classifier that score an example x with: • Z(x): set of possible latent values for x • As for SVM, we learn a classifier by optimizing: = Problem: Non-convex due to considering Z(x)

12. A Discriminatively Trained, Multiscale, Deformable Part Model Overviewing ofTraining models using Latent SVM • Training classifier • Step 1.Holding fixed, optimize the latent values for the positive examples • Step 2.Holding {} fixed for positive examples, optimizeby solving convex problem Become convex when we fix and determinez

13. A Discriminatively Trained, Multiscale, Deformable Part Model Results

14. A Discriminatively Trained, Multiscale, Deformable Part Model Results

15. A Discriminatively Trained, Multiscale, Deformable Part Model Results

16. A Discriminatively Trained, Multiscale, Deformable Part Model Conclusion • Building a detection system based on multiscale, deformable models. • Experimental results on difficult benchmark data support that the performance improvement of the system. (2008) • Training/ Test complexities are quite high due to finding optimal latent variables-> speed up techniques such as cascade approach, linear time searching algorithms are needed.

17. Thank you