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Advanced Techniques in Articulated Pose Estimation and Human Detection

This presentation explores two innovative approaches in computer vision: articulated pose estimation utilizing a flexible mixture of parts, and a robust HOG-LBP human detector designed to manage partial occlusions. The first method, outlined by Yang and Raman, enhances pose estimation by effectively segmenting human figures into more manageable components. The second method, proposed by Wang et al., improves human detection accuracy by incorporating context information that identifies and filters out undesired segments. Both techniques leverage superpixel segmentation for enhanced part localization.

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Advanced Techniques in Articulated Pose Estimation and Human Detection

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  1. Presentation 5 Ruben Villegas 06/26/2012

  2. Papers • Articulated pose estimations with flexible mixture-of-parts, by Yin Yang and Deva Raman • An HOG-LBP Human Detector with Partial Occlusion Handling, by Xiaoyu Wang, Tony X. Han and Shuicheng Yang

  3. Articulated pose estimation with flexible mixture-of-parts by Ying Yang

  4. HOG-LBP Human Detector with Partial Occlusion Handling by Xiaoyu Wang

  5. Super Pixel Segmentation • Segment human detection to get better part location • Use context information to detect undesired segments not belonging to detected person

  6. Pedro’s Detection

  7. 50 Super Pixels

  8. 50 Super Pixels

  9. 10 Super Pixels

  10. 10 Super Pixels

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