mlsanityData to 7 Oct 2026

Towards Rich, Portable, and Large-Scale Pedestrian Data Collection

arXiv 2203.01974 ↗First posted 3 Mar 2022cs.CV cs.ROcited by 0

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Abstract (arXiv)

Recently, pedestrian behavior research has shifted towards machine learning based methods and converged on the topic of modeling pedestrian interactions. For this, a large-scale dataset that contains rich information is needed. We propose a data collection system that is portable, which facilitates accessible large-scale data collection in diverse environments. We also couple the system with a semi-autonomous labeling pipeline for fast trajectory label production. We further introduce the first batch of dataset from the ongoing data collection effort -- the TBD pedestrian dataset. Compared with existing pedestrian datasets, our dataset contains three components: human verified labels grounded in the metric space, a combination of top-down and perspective views, and naturalistic human behavior in the presence of a socially appropriate "robot".

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 46 references that are in the map. First posted on arXiv 3 Mar 2022.

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21 of 46 references.

References · 21

  1. 2021ByteTrack: Multi-Object Tracking by Associating Every Detection Boxcited by 812
  2. 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 687
  3. 2020Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Predictioncited by 324
  4. 2018SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraintscited by 296
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  6. 2018Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learningcited by 162
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  8. 2018Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learningcited by 101
  9. 2021Core Challenges of Social Robot Navigation: A Surveycited by 97
  10. 2019What the Constant Velocity Model Can Teach Us About Pedestrian Motion Predictioncited by 78
  11. 2019The Garden of Forking Paths: Towards Multi-Future Trajectory Predictioncited by 72
  12. 2017Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learningcited by 50
9 more
  1. 2019TH"OR: Human-Robot Navigation Data Collection and Accurate Motion Trajectories Datasetcited by 36
  2. 2019Robot Navigation in Crowds by Graph Convolutional Networks with Attention Learned from Human Gazecited by 27
  3. 2021SocNavBench: A Grounded Simulation Testing Framework for Evaluating Social Navigationcited by 23
  4. 2020Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interactioncited by 21
  5. 2020SEAN: Social Environment for Autonomous Navigationcited by 18
  6. 2021Group-based Motion Prediction for Navigation in Crowded Environmentscited by 15
  7. 2021Move Beyond Trajectories: Distribution Space Coupling for Crowd Navigationcited by 10
  8. 2019Dynamic Channel: A Planning Framework for Crowd Navigationcited by 8
  9. 2019Fast Visual Object Tracking with Rotated Bounding Boxescited by 5

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