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Leonid Pishchulin Arjun Jain Mykhaylo Andriluka Thorsten Thorm¨ahlen Bernt Schiele Max Planck Institute for Informatics, Saarbr¨ucken, Germany.

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Presentation on theme: "Leonid Pishchulin Arjun Jain Mykhaylo Andriluka Thorsten Thorm¨ahlen Bernt Schiele Max Planck Institute for Informatics, Saarbr¨ucken, Germany."— Presentation transcript:

1 Leonid Pishchulin Arjun Jain Mykhaylo Andriluka Thorsten Thorm¨ahlen Bernt Schiele Max Planck Institute for Informatics, Saarbr¨ucken, Germany

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

3 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

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7 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.Object detection 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.

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

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14 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”

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17  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


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