2022-05330 - Post-Doctoral Research Visit F/M Analysis and modelling of
dense crowd behaviors
Contract type : Fixed-term contract
Level of qualifications required : PhD or equivalent
Fonction : Post-Doctoral Research Visit
About the research centre or Inria department
The Inria Rennes - Bretagne Atlantique Centre is one of Inria's eight centres
and has more than thirty research teams. The Inria Center is a major and
recognized player in the field of digital sciences. It is at the heart of a
rich R&D and innovation ecosystem: highly innovative PMEs, large industrial
groups, competitiveness clusters, research and higher education players,
laboratories of excellence, technological research institute, etc.
This postdoctoral fellowship is part of the H2020 CrowdDNA project
(https:// www. crowddna.eu) which focuses on dense crowds and physical
interactions between individuals in crowds in order to prevent their dangers.
The CrowdDNA project, coordinated by Inria, involves 7 partners in Spain,
Germany, France and the UK. The project mixes crowd studies, modeling and
simulation activities and aims at developing crowd movement analysis
technologies to assist in the management of mass events.
The scientific context of the project is the study and modeling of dense
crowds. The dense crowd is characterized by levels of density that involve
contacts between individuals. The forces exchanged within dense crowds pose
potential dangers for individuals with risks of falling or choking 1,2.
Different from social interactions which are distant 3, the study of
physical interactions requires specific approaches for analysis 4,5 or for
modeling and simulation 6,7,8. Recent work also suggests that dense crowds
in a situation of potential danger exhibit characteristic behavioral traits
9,10. The proposed postdoctoral fellowship is a continuation of this work.
Through modeling and analysis activities within the project as a whole, as
well as field campaigns (see objective below), we attempt to develop these
technologies for detecting characteristics of dangerous crowds.
Within the framework of this project, we are conducting field campaigns for
the acquisition of video and motion capture data on crowd movements. The main
objective of the postdoctoral fellowship is to participate to the realization
of these acquisition campaigns and to the exploitation of the data to measure
the activity of a crowd and to characterize situations of potential danger.
The objective of the post-doctorate is therefore at the border of the study
and modeling of dense crowds from field data and the analysis of images of
crowds in movement to extract their characteristics. The precise direction
followed by the recruited person will depend on his/her research profile.
This postdoctoral position offers a unique research environment, within a
transdisciplinary research team, mixing Computer Science, Physics and
Biomechanics. We provide access to unique datasets capturing dense crowd
behaviors, with great potential for original research exploiting them. We have
the necessary degrees of freedom to adapt the direction of this postdoctoral
fellowship to the profile of the person recruited.
1 Helbing, D., Johansson, A., & Al-Abideen, H. Z. (2007). Dynamics of
crowd disasters: An empirical study. Physical review E, 75(4), 046109.
2 Lee, R. S., & Hughes, R. L. (2005). Exploring trampling and crushing
in a crowd. Journal of transportation engineering, 131(8), 575-582.
3 Kaup, D. J., Clarke, T. L., Malone, L., & Oleson, R. (2006). Crowd
dynamics simulation research.
4 Boominathan, L., Kruthiventi, S. S., & Babu, R. V. (2016, October).
Crowdnet: A deep convolutional network for dense crowd counting. In
Proceedings of the 24th ACM international conference on Multimedia (pp.
5 Sundararaman, R., De Almeida Braga, C., Marchand, E., & Pettre, J.
(2021). Tracking pedestrian heads in dense crowd. In Proceedings of the
IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp.
6 Narain, R., Golas, A., Curtis, S., & Lin, M. C. (2009). Aggregate
dynamics for dense crowd simulation. In ACM SIGGRAPH Asia 2009 papers (pp.
7 Kim, S., Guy, S. J., Hillesland, K., Zafar, B., Gutub, A. A. A., &
Manocha, D. (2015). Velocity-based modeling of physical interactions in
dense crowds. The Visual Computer, 31(5), 541-555.
8 van Toll, W., Chatagnon, T., Braga, C., Solenthaler, B., & Pettré, J.
(2021). SPH crowds: Agent-based crowd simulation up to extreme densities
using fluid dynamics. Computers & Graphics, 98, 306-321.
9 Bottinelli, A., Sumpter, D. T., & Silverberg, J. L. (2016). Emergent
structural mechanisms for high-density collective motion inspired by human
crowds. Physical review letters, 117(22), 228301.
10 Bottinelli, A., & Silverberg, J. L. (2017). How to: Using mode
analysis to quantify, analyze, and interpret the mechanisms of high-density
collective motion. Frontiers in Applied Mathematics and Statistics, 3, 26.
In coordination with the CrowdDNA team, the postdoctoral fellow participates
in the design of experiments on crowd behavior and the exploration of the
experimental data collected. He/she will develop methods to analyze these data
in order to better understand the behavior of dense crowds. He designs and
tests new crowd simulation methods.
The recruit will work closely with Julien Pettré, CrowdDNA project
coordinator. He/she will also work with the whole team in Rennes, and the
partners of the project.
Design and implementation of experiments on the behavior of dense crowds
Analysis of experimental data
Design of simulation methods for dense crowds
Writing of scientific papers, scientific communication
Technical skills and level required : PhD
Languages : fluent english
Relational skills : high, to enable collaboration with the CrowdDNA consortium
Partial reimbursement of public transport costs
Leave: 7 weeks of annual leave + 10 extra days off due to RTT
Possibility of teleworking ( 90 days per year) and flexible organization
of working hours
Professional equipment available (videoconferencing, loan of computer
Social, cultural and sports events and activities
Social security coverage
Monthly gross salary amounting to 2746 euros.
Theme/Domain : Vision, perception and multimedia interpretation
Scientific computing (BAP E)
Town/city : Rennes
Inria Center : CRI Rennes - Bretagne Atlantique
Starting date : 2022-12-01
Duration of contract : 2 years
Deadline to apply : 2022-10-16
Inria Team : RAINBOW
Pettre Julien / email@example.com
The keys to success
We are looking for various profiles for this position:
or a thesis on the analysis, modeling or simulation of crowds, from the
field of Computer Science or Physics
or a thesis on crowd image analysis, from the field of Computer Vision
Strong motivation for independent research on crowd behavior
Demonstrated ability to collaborate and communicate scientifically
Inria is the French national research institute dedicated to digital science
and technology. It employs 2,600 people. Its 200 agile project teams,
generally run jointly with academic partners, include more than 3,500
scientists and engineers working to meet the challenges of digital technology,
often at the interface with other disciplines. The Institute also employs
numerous talents in over forty different professions. 900 research support
staff contribute to the preparation and development of scientific and
entrepreneurial projects that have a worldwide impact.
Instruction to apply
Please submit online : your resume, cover letter and letters of recommendation
For more information, please contact firstname.lastname@example.org
Defence Security :
This position is likely to be situated in a restricted area (ZRR), as
defined in Decree No. 2011-1425 relating to the protection of national
scientific and technical potential (PPST).Authorisation to enter an area is
granted by the director of the unit, following a favourable Ministerial
decision, as defined in the decree of 3 July 2012 relating to the PPST. An
unfavourable Ministerial decision in respect of a position situated in a ZRR
would result in the cancellation of the appointment.
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