A Computer Vision Approach For The Load Time History Estimation Of Lively Individuals And Crowds

A computer vision approach for measuring the load time history due to individuals and crowds jumping and bobbing is investigated. The method comprises of tracking the displacement trajectories of individuals and crowds using optical flow based algorithms followed by generating force time histories. Laboratory experiments, in which individuals and groups perform jumping at regular beats and songs on a force platform and on a grandstand simulator, are conducted. The estimated trajectories are compared directly with conventional sensors as well as indirectly with responses acquired from finite element models. The method is further validated via a field demonstration. Limitations of the method and future work for improvement are discussed. The proposed methods along with their applications on a real structure, and findings from a laboratory grandstand simulator that can accommodate experiments for groups of different sizes and structural configurations show great promise for computer vision based load modeling. In this sense, the study is taking an important step in support of creating a database for crowd loading that is needed as it is pointed out in the literature