In　this　paper,　we　propose　an　approach　to　estimate　and　quantify　the　degree　of　extraversion　for　crowd　motion　based　on　individual　trajectories.　Extraversion　is　a　typical　personality　that　is　often　observed　in　human　behaviors.　We　present　a　composite　motion　descriptor,　which　integrates　the　basic　motion　information　and　social　metrics,　to　describe　the　extraversion　of　each　individual　in　a　crowd.　In　order　to　train　a　universal　scoring　function　that　can　measure　the　degrees　of　extraversion,　we　incorporate　the　active　learning　technique　with　the　relative　attribute　approach　based　on　the　social　grouping　behavior　in　crowd　motions.　In　addition,　we　demonstrate　the　performance　of　the　proposed　method　by　measuring　the　degree　of　extraversion　for　real　individual　trajectories　in　a　crowd　and　analyzing　crowd　scenes　from　a　real-world　dataset.