[robotics-worldwide] [meetings] Call for Submissions: TrajNet++ -- A Trajectory Forecasting Challenge
Palmieri Luigi (CR/AER1)
Luigi.Palmieri at de.bosch.com
Tue Mar 17 07:35:39 PDT 2020
Call for Submissions to TrajNet++ Challenge
2nd Workshop on Long-term Human Motion Prediction
Challenge Deadline: May 17, 2020, 23:59 CEST
Challenge URL: https://urldefense.com/v3/__https://www.aicrowd.com/challenges/trajnet-a-trajectory-forecasting-challenge__;!!LIr3w8kk_Xxm!_a1K7E2RpXIFqGM7Q-BERVxwazuRnYumoOPDTFKDF4ntlFP0g0BovE8sLYeADsQz4_BwSE0u$
Results will be presented and discussed during the ICRA 2020 -- 2nd Workshop on Long-term Human Motion Prediction
ICRA 2020 Workshop, May 31, 2020
Location: Paris, France
Workshop URL: https://urldefense.com/v3/__https://motionpredictionicra2020.github.io__;!!LIr3w8kk_Xxm!_a1K7E2RpXIFqGM7Q-BERVxwazuRnYumoOPDTFKDF4ntlFP0g0BovE8sLYeADsQz44ooKLh8$
Anticipating human motion is a key skill for intelligent systems that share a space or interact with humans. Accurate long-term predictions of human movement trajectories, body poses, actions or activities may significantly improve the ability of robots to plan ahead, anticipate the effects of their actions or to foresee hazardous situations. The topic has received increasing attention in recent years across several scientific communities with a growing spectrum of applications in service robots, self-driving cars, collaborative manipulators or tracking and surveillance. The aim of this workshop is to bring together researchers and practitioners from different communities and to discuss recent developments in this field, promising approaches, their limitations, benchmarking techniques and open challenges. The program includes ten invited speakers, a poster session with spotlight talks and the TrajNet++ Challenge.
The future brings forth a promise to make transportation safer and easier with the advent of Autonomous systems. In order to successfully navigate through diverse scenarios, these systems need to understand the rules of human motion regarding social interactions and physical interactions. These social etiquettes come intuitively to humans through years of observation. A task closely related to understanding human motion is forecasting the movement of the surrounding people, conforming to the common sense unspoken rules of human motion as well as the surrounding physical constraints. In this workshop, we are running a challenge on trajectory forecasting focused on modeling agent-agent interactions referred to as TrajNet++ challenge.
In the past few years, several novel methods have been proposed to tackle agent-agent interactions. However, most methods have been evaluated on limited data. Furthermore, these methods have been evaluated on different subsets of the available data without proper indexing of trajectories making it difficult to objectively compare the forecasting techniques. In order to tackle this issue, we propose a large-scale framework that provides not only proper indexing of trajectories but also a unified extensive evaluation system to test the gathered methods for a fair comparison. In this agent-agent challenge, researchers have to study how their method performs in explicit agent-agent scenarios.
Detailed submission information for the challenge can be found on: https://urldefense.com/v3/__https://www.aicrowd.com/challenges/trajnet-a-trajectory-forecasting-challenge__;!!LIr3w8kk_Xxm!_a1K7E2RpXIFqGM7Q-BERVxwazuRnYumoOPDTFKDF4ntlFP0g0BovE8sLYeADsQz4_BwSE0u$
Contact for questions related to the challenge: Kothari Parth <parth.kothari at epfl.ch>
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