PPT-Articulated People Detection and Pose Estimation: Reshaping

Leonid Pishchulin Arjun Jain Mykhaylo Andriluka Thorsten Thormahlen Bernt Schiele Max Planck Institute for Informatics Saarbrucken Germany Introduction Generation of novel training . Rather than modeling articulation using a family of warped rotated and foreshortened templates we use a mixture of small nonoriented parts We describe a general 64258exible mixture model that jointly captures spatial relations between part locations After a brief introductionmotivation for the need for parts the bulk of the chapter will be split into three core sections on Representation Inference and Learning We begin by describing various gradient based and color descriptors for parts We will berkeleyedu lubomirfbcom Figure 1 An example of an image where part detectors based solely on strong contours and edges will fail to detect the upper and lower parts of the arms Abstract We propose a novel approach for human pose estimation in realwo Our approach is examplebased it reduces the problem of recovering the pose to a database search under in the embedding space which is carried out extremely fast using LSH The embedding is constructed based on edge direction histograms using the algo Eichner M MarinJimenez A Zisserman V Ferrari Abstract We present a technique for estimating the spatial layout of humans in still images the position of the head torso and arms The theme we explore is that once a per son is localized using an upper Tracking . and Head Pose Estimation for Gaze Estimation. Ankan Bansal. Salman Mohammad. CS365 Project. Guide - Prof. . Amitabha Mukerjee. Motivation. Human Computer Interaction. Information about interest of the subject, e.g. advertisement research. Yi Yang & Deva . Ramanan. University of California, Irvine. Goal. Articulated pose . estimation. ( ). recovers . the pose of an articulated object which consists of joints and rigid parts. Yu Chen, Tae-. K. yun. Kim, Roberto . Cipolla. Department of Engineering. University of Cambridge. Roadmap. Brief Introductions. Our Framework. Experimental Results. Summary. Motivation. +. 3D Shapes. Yao Li . Fei-Fei. Computer Science Department, Stanford University, USA. Modeling Mutual Context of Object and Human Pose. in Human-Object Interaction Activities. Introduction. Modeling mutual context of object and pose. Hamed Pirsiavash and Deva . Ramanan. Department of Computer Science. UC Irvine . 2. Deformable . part models . (DPM). Human pose estimation. Face pose estimation. Object detection. Felzenszwalb. , . Girshick. Transductive. Regression Forests . Tsz-Ho. Yu. Danhang. . Tang. T-K. Kim. Sponsored by . 2. Motivation. Multiple cameras with invserse kinematics. [Bissacco et al. CVPR2007]. [Yao et al. IJCV2012]. Bangpeng Yao and Li Fei-Fei. Computer Science Department, Stanford University. {bangpeng,feifeili}@cs.stanford.edu. 1. Robots interact with objects. Automatic sports commentary. “Kobe is dunking the ball.”. Bangpeng Yao and Li Fei-Fei. Computer Science Department, Stanford University. {bangpeng,feifeili}@cs.stanford.edu. 1. Robots interact with objects. Automatic sports commentary. “Kobe is dunking the ball.”. Rendevous. using CNN. Ryan McKennon-Kelly. Sharma, . Sumant. , Connor . Beierle. , and Simone D’Amico. “Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks,” September 19, 2018. .