20180704-19 汤思宇:Graph Decomposition for People Tracking and Pose Estimation . ...

报告嘉宾：汤思宇（德国马克斯普朗克研究所）

报告时间：2018年07月04日（星期三）晚上20:00（北京时间）

报告题目：Graph Decomposition for People Tracking and Pose Estimation

主持人：顾舒航（香港理工）

报告人简介：

Siyu Tang is a research group leader in the Department of Perceiving Systems at the Max Planck Institute for Intelligent Systems, Germany. 

She was a postdoctoral researcher at the Max Planck Institute for Intelligent Systems, advised by Michael Black. She finished her PhD (summa cum laude) at the Max Planck Institute for Informatics, under the supervision of Prof. Bernt Schiele. Before that, she received Master degree in Computer Science at RWTH Aachen University, advised by Prof. Bastian Leibe and Bachelor degree in the Computer Science and Technology Department at Zhejiang University, China. She was a research intern at the National Institute of Informatics, under the supervision of Prof. Helmut Prendinger. 

Her research concerns the intersection between computer vision and machine learning with a focus on holistic visual scene understanding. In particular, she is interested in analyzing and modeling people in our complex visual scenes.

个人主页：

https://ps.is.tuebingen.mpg.de/person/stang

报告摘要：

Understanding people in images and videos is a problem studied intensively in computer vision. While continuous progress has been made, occlusions, cluttered background, complex poses and large variety of appearance remain challenging, especially for crowded scenes. In this talk, I will explore the algorithms and tools that enable computer to interpret people's position, motion and articulated poses in complex visual scenes. More specifically, I will discuss an optimization problem whose feasible solutions relate one-to-one to the decompositions of a graph. I will highlight the applications of this problem in computer vision, which range from multi-person tracking to motion segmentation. I will also cover an extended optimization problem whose feasible solutions define the decomposition of a graph and the labeling of its nodes with the application on multi-person pose estimation.

参考文献：

[1] End-to-end Learning for Graph Decomposition. under submission. Multi-person Tracking with Lifted Multicut and Person Re-ID. CVPR 17. Articulated Multi-person Tracking in the Wild. CVPR 17.

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VODB协调理事：左旺孟（哈尔滨工业大学）

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[slides]