Anticipating Human Behavior

Bonn, Germany, 2nd September 2019

About

In contrast to humans that are very good in anticipating the behavior of other objects, animals, or humans, developing methods that anticipate human behavior from video or other sensor data is very challenging and has just recently received an increase of interest. In the past, the features for analyzing, in particular, visual data like images or videos were too weak such that approaches that predict the future were unlikely to succeed.

This burden has been overcome due to recent progress in this field. The anticipation of human behavior, however, is not well defined in the literature and varies depending on the task in terms of granularity and time horizon. In the context of driver assistance systems, the prediction of the trajectory of a pedestrian needs to be within centimeter accuracy but only for a very short time horizon of one second.
For tracking applications or motion planning, the potential destination of a human and trajectories of several seconds or minutes to reach the destination need to be predicted. In order to prioritize several tasks for a service robot during a day, only the rough time and location of an activity is needed. For instance, when the robot anticipates that the owner wants to cook in one hour, the robot will be in the kitchen at the right time.

The purpose of this workshop is to discuss recent approaches that anticipate human behavior from video or other sensor data, to bring together researchers from multiple fields and perspectives, and to discuss major research problems and opportunities and how we should coordinate efforts to advance the field.
The workshop will be located in the lecture hall HS2 at the University of Bonn, Campus Poppelsdorf, Endenicher Allee 19a, 53115 Bonn.

The workshop is organized as part of the DFG funded research unit FOR 2535 - Anticipating Human Behavior and a previous workshop was held in Munich 2018.

Dates

Workshop 02.09.2019

Registration

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Speakers

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Programm

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Contact

Prof. Dr. Jürgen Gall

University of Bonn

Institute of Computer Science

Computer Vision Group

Endenicher Allee 19a

53115 Bonn, Germany

E-mail: gall@iai.uni-bonn.de

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Image source: Poppelsdorf Palace © Dr. Thomas Mauersberg / University of Bonn