Data driven evaluation of crowds
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Data Driven Evaluation of Crowds. Presenter: Robin van Olst. The Authors. Professor Ariel Shamir. Assistant Professor Yiorgos Chrysanthou. Professor Daniel Cohen-Or. PhD. Alan Lerner. What is it about?. Crowd simulation quality is usually judged subjectively Based on ‘look-&-feel’

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Data Driven Evaluation of Crowds

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Data driven evaluation of crowds

Data Driven Evaluation of Crowds

Presenter: Robin van Olst

The authors

The Authors

Professor Ariel Shamir

Assistant Professor Yiorgos Chrysanthou

Professor Daniel Cohen-Or

PhD. Alan Lerner

What is it about

What is it about?

  • Crowd simulation quality is usually judged subjectively

    • Based on ‘look-&-feel’

    • Multiple definitions of ‘natural behavior’ are possible

  • Authors propose an objective approach

Previous work

Previous work

  • State-action examples

    • Group behavior from video: a data-driven approach to crowd simulation – Lee et al.

    • Crowds by Example – Lerner et al.

  • Analyzing motion data for validation

    • Pedestrian Reactive Navigation for Crowd Simulation: a predictive Approach – Paris et al.

  • Vision community’s work?

    • Doesn’t look at the quality of trajectory segments



  • State-action examples

    • One for each agent, at a specific time and space

    • Holds data

      • Position, speed and direction of the agent

      • Position of nearby agents

  • Input videos

    • Analysis produces state-action examples

      • Are entered in a database

  • Evaluator

    • Everything is known (state attributes, trajectories)

    • Compares the action performed vs. action that should have been performed

      • Rates similarity to most similar state-action





  • Positive points

  • Negative points

  • Conclusion

Positive points

Positive points

  • Appears to be one of the first papers regarding objective crowd simulation judgement

  • Takes advantage of emperical data

  • Is able to find‘curious’ behavior:



  • Positive points

  • Negative points

  • Conclusion



  • Analysis performance

    • 12 minutes of a sparse crowd, 343 trajectories

      • Took almost an hour

    • 3,5 minutes of a dense crowd, 434 trajectories

      • Took more than a hour

    • State-action data is ~1KB large

      • Unknown how much data is generated

  • Impossible to check large crowds or crowds for an extended time?

    • Requires more video data

Captured video data

Captured video data

Technical issues:

  • Field of view must be fairly small

    • Wide or distant view may be too inaccurate

  • No obstructions are allowed

    • Doesn’t translate to real life

  • Can existing data be used?

Captured video data1

Captured video data

Practical issues:

  • Manual tracking is tedious

    • Is automatic tracking accurate enough?

  • No obstructions are allowed

    • Doesn’t translate to real life

  • Video analysis comparison is verification, not falsification



  • Doesn’t consider grouping?

  • Only really works for existing environments

    • New environments require new videos

  • Doesn’t indicate how the tested crowd simulation should improve

  • Can’t be used to compare crowd simulation methods

    • Good evaluation depends on the quality of your input video



  • No video or meaningful results

  • Referenced by one paper

    • Context‐Dependent Crowd Evaluation – by Lerner et al.

      • Referenced by none

    • Does not appear on any of the authors’ publication section

  • Verdict: only useful for checking your crowd simulation

    • Even that is cumbersome

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