Overview of the Crowd Analysis Setup

This repository accompanies an academic publication, and an updated, improved version of the Crowdbot dataset. It provides a reproducible analysis pipeline for pedestrian behavior in crowds. By analyzing motion metrics and proxemics, we study differences between human–human interactions (HHI) and human–robot interactions (HRI) in crowded public spaces across the Crowdbot, JRDB, and SiT datasets.

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Files & Folders

The repository layout is as follows (key items):

• AB3DMOT/ — LiDAR-based tracking (package:ab3dmot), original repo: https://github.com/xinshuoweng/AB3DMOT
• checkpoints/ — pre-trained detector weights (e.g., DR-SPAAM, Person_MinkUNet). Uploaded together with the dataset.
• crowd_analysis/
  • crowd_behavior.ipynb — main analysis notebook with all motion metrics and proxemics analysis
• datasets_configs/ — dataset configuration YAMLs
  • data_path_Crowdbot.yaml
  • data_path_JRDB.yaml
  • data_path_SiT.yaml
• datasets_utils/ — dataset utilities (package:crowdbot_data) used by both environments
• lidar_det_2D_3D/ — LiDAR detection (package:lidar_det) combining
  • Person_MinkUNet (3D): https://github.com/VisualComputingInstitute/Person_MinkUNet
  • DR-SPAAM (2D): https://github.com/VisualComputingInstitute/DR-SPAAM-Detector
• rosbags_extraction/ — scripts for ROS bag processing
  • 1_Lidar_from_rosbags.py
  • 2_Pose_from_rosbags.py
  • 3_Detections_from_lidar.py
  • 4_Tracks_from_detections.py
  • Extract_gt_JRDB.py
  • Extract_SiT.py
• run_pipeline.sh — script with a full processing pipeline for generating the input data
• requirements.txt — Python packages installed into crowd_env

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Dataset

Structure

graph LR;
ROOT[Dataset root];
ROOT-->CK[checkpoints/];
CK-->CKP[*.pth];
ROOT-->RB[rosbags/];
RB-->RBX[_defaced/];
RBX-->BAGS[.bag];
ROOT-->PR[processed/];
PR-->PRX[*_processed/];
PRX-->ALG[alg_res/];
ALG-->DET[detections/];
ALG-->TRK[tracks/];
PRX-->L3D[lidars/];
PRX-->L2D[lidars_2d/];
PRX-->PED[ped_data/];
PRX-->SRC[source_data/];
SRC-->TF[tf_robot/];
SRC-->TS[timestamp/];

Loading

Demo

Item	Preview
(a) Pedestrian trajectories
(b) Motion metrics distributions
(c) Minimum distance distributions
(d) Linear minimum distance vs. robot velocity

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Proposed/Recommended environment setup with tested package versions

Python version used: 3.8.10

Two Conda environments are used:

• ros_env — ROS I/O from rosbags (bag reading, TF transforms, message types).
• crowd_env — Deep-learning detection/tracking + analysis/visualization.

1) Create ros_env (RoboStack Noetic)

RoboStack brings ROS Noetic into Conda directly (guide: https://robostack.github.io/noetic.html).

mamba create -n ros_env -c conda-forge -c robostack-noetic ros-noetic-desktop ros-noetic-tf2-sensor-msgs
mamba activate ros_env
# Minimal math/transforms used by ros-side scripts
pip install scipy==1.16.2 numpy-quaternion==2024.0.12

RoboStack already provides the compiled message/runtime bits; no extra apt is needed.

2) Create crowd_env

Create the environment (CUDA 11.8 + PyTorch 2.0.0 as tested):

mamba create -n crowd_env python=3.8.10 ipykernel cuda-toolkit pytorch==2.0.0 torchvision==0.15.0 torchaudio==2.0.0 pytorch-cuda=11.8 setuptools=69.5.1 mkl=2023.2.0 mkl-include=2023.2.0 mkl-devel=2023.2.0 -c "nvidia/label/cuda-11.8.0" -c pytorch -c nvidia
mamba activate crowd_env
# Install remaining packages for crowd_env
pip install -r requirements.txt

TorchSparse (install from source — version 2.0.0)

torchsparse==2.0.0 is required for 3D detection and must be installed from source. See the official repository/instructions:
https://github.com/mit-han-lab/torchsparse

Ensure your PyTorch CUDA version is compatible (this setup uses CUDA 11.8 with PyTorch 2.0.0).

Local packages (editable installs)

• Install in both ros_env and crowd_env:

  # Dataset Utils (package: crowdbot_data)
  mamba activate ros_env && pip install -e ./datasets_utils
  mamba activate crowd_env && pip install -e ./datasets_utils

• Install only in crowd_env:

  # LiDAR Detection (package: lidar_det)
  pip install -e ./lidar_det_2D_3D
  # internal libs
  pip install -e ./lidar_det_2D_3D/lib/iou3d
  pip install -e ./lidar_det_2D_3D/lib/jrdb_det3d_eval
  # LiDAR-based Tracking (package: ab3dmot) — original repo: https://github.com/xinshuoweng/AB3DMOT
  pip install -e ./AB3DMOT

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Pipeline overview

The repository provides .ipynb and .py processing scripts. They take as input processed rosbags or prepared LiDAR data from Crowdbot, JRDB, and SiT, and produce outputs in a unified Crowdbot data convention for crowd behavior analysis.

Four processing stages

1. 1_Lidar_from_rosbags.py — For Crowdbot and JRDB: extracts 2D/3D LiDAR scans from rosbags and transforms them to the global frame. Saves synchronized LiDAR timestamps. (uses ros_env)
2. 2_Pose_from_rosbags.py — For Crowdbot and JRDB: extracts robot pose, upsamples to 200 Hz, applies smoothing, and computes velocity, acceleration, and jerk. Synchronizes pose timestamps with LiDAR. (uses ros_env)
3. 3_Detections_from_lidar.py — For Crowdbot, JRDB, and SiT: runs 2D (DR-SPAAM) and 3D (Person_MinkUNet) detectors on prepared LiDAR data (no rosbags). Produces 2D-only, 3D-only, and merged close–far detections. (uses crowd_env)
4. 4_Tracks_from_detections.py — For Crowdbot, JRDB, and SiT: builds tracks with AB3DMOT from detections (no rosbags). Produces 2D/3D/merged tracks. (uses crowd_env)

Dataset-specific extractors

• Extract_gt_JRDB.py — extracts ground truth for JRDB only.
• Extract_SiT.py — extracts LiDAR, egomotion, and labels for SiT.

Automated full pipeline

bash run_pipeline.sh

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References:

Please cite both the dataset as well as the publication if you use our dataset/repository in your work.

Crowdbot_v2 dataset

Wojcikiewicz, D., Billard, A., & Paez-Granados, D. (2025). CrowdBot_v2: Pedestrian–Robot crowd navigation dataset with pedestrian tracking (v2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.17694140

Academic Publication

Wojcikiewicz D., Billard A., Paez-Granados D. Assessing pedestrian responses to autonomous and personal mobility robots in crowded public spaces. Science Advances (2026). https://doi.org/10.1126/sciadv.aef2576

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Acknowledgment

This research work was partially supported by the Innosuisse Project 103.421 IP-IC "Developing an AI-enabled Robotic Personal Vehicle for Reduced Mobility Population in Complex Environments" and the JST Moonshot R&D [Grant Number JPMJMS2034-18].