A Discriminatively Trained, Multiscale, Deformable Part Model Yeong-Jun Cho Computer Vision and Pattern Recognition,2008

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

 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 Introduction 3 A Discriminatively Trained, Multiscale, Deformable Part Model

 Object detection and localization – Idea A collection of parts arranged in a deformable configuration Coarse model with detailed models – Challenges Illumination changes Viewpoint Intraclass variability Non-rigid deformation Introduction 4 A Discriminatively Trained, Multiscale, Deformable Part Model Detection results using Deformable part 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: Part-based model 5 A Discriminatively Trained, Multiscale, Deformable Part Model Root filterPart filters Deformation cost

 Simple model  Part-based model Part-based model 6 A Discriminatively Trained, Multiscale, Deformable Part Model z Score : max over components

7 A Discriminatively Trained, Multiscale, Deformable Part Model Part-based model

8 A Discriminatively Trained, Multiscale, Deformable Part Model Part-based model

9 A Discriminatively Trained, Multiscale, Deformable Part Model Part-based model x2 resolution Sum of root and part filters scores Deformation costs of part filters

10 A Discriminatively Trained, Multiscale, Deformable Part Model Part-based model Sum of root and part filters scores Deformation costs of part filters

 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: Overviewing of Training models using Latent SVM 11 A Discriminatively Trained, Multiscale, Deformable Part Model Problem: Non-convex due to considering Z(x)

12 A Discriminatively Trained, Multiscale, Deformable Part Model Overviewing of Training models using Latent SVM

Training models using Latent SVM 13 A Discriminatively Trained, Multiscale, Deformable Part Model

 Training classifier 14 A Discriminatively Trained, Multiscale, Deformable Part Model Overviewing of Training models using Latent SVM

Results 15 A Discriminatively Trained, Multiscale, Deformable Part Model

Results 16 A Discriminatively Trained, Multiscale, Deformable Part Model

Results 17 A Discriminatively Trained, Multiscale, Deformable Part Model

 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. Conclusion 18 A Discriminatively Trained, Multiscale, Deformable Part Model

19 Thank you