Semantic Synthesis of Pedestrian Locomotion

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

Abstract

We present a model for generating 3d articulated pedestrian locomotion in urban scenarios, with synthesis capabilities informed by the 3d scene semantics and geometry. We reformulate pedestrian trajectory forecasting as a structured reinforcement learning (RL) problem. This allows us to naturally combine prior knowledge on collision avoidance, 3d human motion capture and the motion of pedestrians as observed e.g. in Cityscapes, Waymo or simulation environments like Carla. Our proposed RL-based model allows pedestrians to accelerate and slow down to avoid imminent danger (e.g. cars), while obeying human dynamics learnt from in-lab motion capture datasets. Specifically, we propose a hierarchical model consisting of a semantic trajectory policy network that provides a distribution over possible movements, and a human locomotion network that generates 3d human poses in each step. The RL-formulation allows the model to learn even from states that are seldom exhibited in the dataset, utilizing all of the available prior and scene information. Extensive evaluations using both real and simulated data illustrate that the proposed model is on par with recent models such as S-GAN, ST-GAT and S-STGCNN in pedestrian forecasting, while outperforming these in collision avoidance. We also show that our model can be used to plan goal reaching trajectories in urban scenes with dynamic actors.

Details

Authors
Organisations
External organisations
  • University of Bucharest
  • Google Inc.
  • Institute of Mathematics of the Romanian Academy
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Computer Vision and Robotics (Autonomous Systems)
Original languageEnglish
Title of host publicationComputer Vision – ACCV 2020 - 15th Asian Conference on Computer Vision, 2020, Revised Selected Papers
EditorsHiroshi Ishikawa, Cheng-Lin Liu, Tomas Pajdla, Jianbo Shi
PublisherSpringer Science + Business Media
Pages470-487
Number of pages18
ISBN (Print)9783030695316
Publication statusPublished - 2021
Publication categoryResearch
Peer-reviewedYes
Event15th Asian Conference on Computer Vision, ACCV 2020 - Virtual, Online
Duration: 2020 Nov 302020 Dec 4

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12623 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th Asian Conference on Computer Vision, ACCV 2020
CityVirtual, Online
Period2020/11/302020/12/04