Presenter: Robin van Olst

Professor Ariel Shamir PhD. Alan Lerner Professor Daniel Cohen-Or Assistant Professor Yiorgos Chrysanthou

 Crowd simulation quality is usually judged subjectively ◦ Based on ‘look-&-feel’ ◦ Multiple definitions of ‘natural behavior’ are possible  Authors propose an objective approach

 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

 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

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?

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