Post-Doctoral Research Visit F/M Crowd dynamics data acquisition and processing for large-scale dataset construction

1000scholars; Rennes; On-site; EUR 42,000 - 54,000; Full time

Job summary

The VirtUs team at the Inria Centre in Rennes is seeking a postdoctoral researcher to contribute to the FOUL-X project. The role focuses on building an open dataset documenting diverse crowd dynamics in real-world public spaces and on developing a video-based pedestrian tracking pipeline used on field recordings.

The successful candidate will design and execute field campaigns across multiple sites in France, validate and structure trajectories, and develop metrics to compare crowd dynamics

Qualifications

• Technical skills: video-based data processing and object tracking methods.
• Programming in Python.
• Experience with real-world data acquisition and processing pipelines.

Responsibilities

• Design and execute field acquisition campaigns across multiple sites in France.
• Validate and structure the resulting trajectory dataset.
• Develop metrics to characterize and compare crowd dynamics.
• Ensure open dissemination and public availability of the FOUL-X dataset.
• Coordinate field missions including logistical, technical, and ethical aspects.

Skills

Video processing

Object tracking

Python

Data pipelines

Job description

Context

The VirtUs team at the Inria Centre at the University of Rennes is internationally recognized for its work in crowd simulation and the study of collective human behaviour. This postdoctoral position is part of the FOUL-X project (Programme Inria Quadrant), which aims to develop a new generation of crowd simulators capable of capturing the specific dynamics of crowds in real-world public spaces.

A key challenge in crowd simulation is the lack of datasets documenting the variety of crowd dynamics observed in different environments. Existing datasets are sparse and rarely capture the diversity of behaviours that emerge from different populations, activities, and spatial configurations. FOUL-X addresses this gap by designing and conducting field acquisition campaigns across multiple sites in France, with the goal of building an open, large-scale dataset of crowd dynamics.

This postdoc focuses on the data acquisition pipeline: from video capture in the field to the extraction of individual trajectories, and the characterization of crowd dynamics through dedicated metrics. The work will contribute directly to making the FOUL-X dataset available to the broader scientific community.

Assignment

With the help of the VirtUs team and under the supervision of Julien Pettré, the recruited person will be tasked with building a unique, open dataset documenting the diversity of crowd dynamics observed in real-world public spaces. This dataset will constitute a landmark contribution to the field, providing the scientific community with data capturing crowd behaviours across a variety of sites, populations, and spatial configurations — something that does not currently exist at this scale and diversity.

For a better knowledge of the proposed research subject: A state of the art, bibliography and scientific references are available on the VirtUs team website: https://www.inria.fr/en/virtus

Collaboration

The recruited person will work in close connection with a PhD student of the VirtUs team, who develops the video-based pedestrian tracking pipeline used to extract individual trajectories from field recordings, and with the second postdoctoral researcher of the FOUL-X project, who is responsible for the data-driven modelling activities. This triangular collaboration ensures that the dataset is built in direct response to both the technical constraints of the tracking pipeline and the scientific requirements of the modelling work.

Responsibilities

The person recruited is responsible for the design and execution of field acquisition campaigns across multiple sites in France, the validation and structuring of the resulting trajectory dataset, and the development of metrics to characterise and compare the diversity of observed crowd dynamics. The recruited person will take initiatives to maximise the scientific value of the dataset and ensure its open dissemination to the community.

Steering/Management

The person recruited will be in charge of coordinating field missions — including logistical, technical, and ethical aspects of data capture — and will lead the effort to make the FOUL-X dataset publicly available in a reusable and well-documented form.

Main activities

1. Phase 1 — Pipeline setup and campaign preparation (months 1–6)
   • Evaluate and validate the video-based trajectory extraction pipeline developed by the PhD student of the team, with respect to crowd density, resolution constraints, and GDPR compliance requirements
   • Define the minimal data resolution required to extract complete and accurate individual trajectories while ensuring data anonymisation
   • Contribute to the identification and selection of acquisition sites, targeting a diversity of crowd dynamics (populations, spatial configurations, activities)
   • Participate in the preparation of the ethical framework and site agreements for field data collection
2. Phase 2 — Field acquisition and dataset construction (months 7–18)
   • Lead and coordinate field acquisition campaigns across 4 to 6 sites in France, including on-site deployment of video capture equipment
   • Supervise the processing of raw video data into structured trajectory datasets using the tracking pipeline
   • Iteratively validate the quality, completeness and robustness of extracted trajectories
   • Structure and document the dataset for internal use and future open dissemination
   • Contribute to the first scientific dissemination of results (e.g. PED 2027 conference)
3. Phase 3 — Dataset characterisation and metrics (months 19–24)
   • Develop quantitative metrics to characterise and compare the diversity of crowd dynamics across acquisition sites
   • Contribute to the analysis of differences between crowd behaviours at multiple spatial and temporal scales
   • Finalise the open release of the FOUL-X dataset on a dedicated public platform
   • Contribute to a major publication presenting the dataset and its characterisation

Skills

Technical skills (required):

• Video-based data processing and object tracking methods
• Programming in Python
• Experience with real-world data acquisition and processing pipelines

Technical skills (a plus):

• Familiarity with pedestrian trajectory analysis
• Knowledge of GDPR and ethical frameworks for data collection in public spaces
• Experience with open dataset publication and documentation

Languages:

• English (required for scientific dissemination)
• French (a plus for interactions with acquisition sites)

Relational skills:

• Strong organisational and coordination skills
• Ability to work in a collaborative and interdisciplinary environment
• Autonomy and initiative in managing field campaigns