A Human and Group Behaviour Simulation Evaluation Framework utilising Composition and Video Analysis
This work addresses the need for standardized evaluation in crowd simulation research, though it appears incremental as it builds on existing composition and analysis techniques.
The authors tackled the problem of evaluating pedestrian and crowd simulation algorithms by developing the CSEC framework, which uses video composition and human visual system features to quantitatively compare synthetic videos with source footage, resulting in a modular platform for algorithm comparison and tuning.
In this work we present the modular Crowd Simulation Evaluation through Composition framework (CSEC) which provides a quantitative comparison between different pedestrian and crowd simulation approaches. Evaluation is made based on the comparison of source footage against synthetic video created through novel composition techniques. The proposed framework seeks to reduce the complexity of simulation evaluation and provide a platform from which the comparison of differing simulation algorithms as well as parametric tuning can be conducted to improve simulation accuracy or providing measures of similarity between crowd simulation algorithms and source data. Through the use of features designed to mimic the Human Visual System (HVS), specific simulation properties can be evaluated relative to sample footage. Validation was performed on a number of popular crowd datasets and through comparisons of multiple pedestrian and crowd simulation algorithms.