In the high-density crowd flow places in public buildings, typical mobile obstacles, such as trolley cases, mobile sweeping trolleys, shuttle trolleys, police patrol cars, etc., carried by passengers bring convenience for passengers to travel, and can also act as typical obstacles that hinder the flow of people. It is easy to block the flow of people, cause the crowd to become unstable, and cause overcrowding and even stampede accidents. To study the influence of moving obstacles on crowd stability, this paper analyzes the spatial and moving characteristics of typical moving obstacles and constructs a motion model of moving obstacles. Furthermore, based on smooth particle hydrodynamics (SPH), a coupled macroscopic pedestrian flow model including moving obstacles and pedestrian flow is proposed. In order to verify the effectiveness of this proposed coupled motion model, this study takes trolley luggage as an example to design and implement a moving obstacle experiment in pedestrian flow, exploring the impact of moving obstacles to the pedestrian flow, further to study the stability of pedestrian flow.
Experiment design and implement to detect the possible pedestrian abnormal-behaviors in cross passages of public buildings are more significant to prevent possible crowd accidents than ever before. The further support of abnormal-behavior experiments can be helpful to stability analysis of moving pedestrian crowds. To summarize the experiments on pedestrian abnormal behavior detection based on computer vision technology, this study focuses both on the abnormal behaviors of moving pedestrians in public traffic areas and the computer vision technologies. A 3D scene analysis workflow using computer vision for crowd behavior experiment is designed. The Workflow model of abnormal behavior recognition and stability analysis in crowd movement used in experiment design is proposed based on Lyapunov criterion theory. Finally, a survey table of typical abnormal behaviors in public scenes is figured out.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.