Articulated People Detection and Pose Estimation: Reshaping the Future

1. Articulated People Detection and Pose Estimation: Reshaping the Future Leonid PishchulinArjun JainMykhayloAndriluka Thorsten Thorm¨ahlenBerntSchiele Max Planck Institute for Informatics, Saarbr¨ucken, Germany

2. OUTLINE • Introduction • Generation of novel training examples • Articulated people detection • Articulated pose estimation • Articulated pose estimation “in the wild” • Conclusion

3. Introduction Recent progress in people detection and articulated pose estimation may be contributed to two key factors. • Discriminative learning allows to learn powerful models on a large training corpora • robust image features enable to deal with image clutter, occlusions and appearance variation

4. Introduction

5. Generation of novel training examples

6. Generation of novel training examples

7. Articulated people detection We use the deformable part model (DPM) [11] and evaluate its performance on the “Image Parsing” dataset [25]. For training we use training sets from the publicly available datasets: • DPM-VOC PASCAL VOC 2009 (VOC) [10] • DPM-IP “Image Parsing”(IP) [25] • DPM-LSP “Leeds Sports Poses” (LSP)dataset [19] • DPM-IP-R and DPM-IP-AR [10] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL visual object classes (VOC) challenge.IJCV’10. [11] P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan.Objectdetection with discriminatively trained part-based models.PAMI’10. [19] S. Johnson and M. Everingham. Clustered pose and nonlinear appearance models for human pose estimation. In BMVC’10. [25] D. Ramanan. Learning to parse images of articulated objects. In NIPS’06.

8. Articulated people detection

9. Articulated people detection

10. Articulated people detection

11. Articulated pose estimation • Proposes a new joint model for body pose estimation combining pictorial structures [12,14]model with DPM

12. Articulated pose estimation

13. Articulated pose estimation

14. Articulated pose estimation “in the wild” We define a new dataset based on the LSP by using the publicly available original non-cropped images. This dataset, in the following denoted as “multi-scale LSP”

15. Articulated pose estimation “in the wild”

16. Articulated pose estimation “in the wild”

17. Conclusion • Propose a novel method for automatic generation of training examples • Evaluate our data generation method for articulated people detection and pose estimation and show that we significantly improve the performance • Propose a joint model

18. Thank you for listening