In public traffic scenarios, the old pedestrians, as the vulnerable and care-needing people, often have serious and irreversible effects on their bodies once accidents such as falls occur in their lives. To recognize the dangerous falling behavior of old pedestrians in public transportation buildings, this paper analyzes the gait frequency characteristics of the old pedestrians, tracks specific old pedestrians, adopts the kinematic stability theory, and obtains the kinematic characteristics of video image information based on computer vision technology. Finally, this study builds a kinematics model for the recognition of the fall behavior of the old pedestrians, which can provide an invisible safety guarantee for the safe travel of the old pedestrians.
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.
In public places, the fall behaviors of pedestrians possibly lead to the disturbances of the crowd, and even cause stampedes. This study deals with the problem of identifying anomalous pedestrian behavior in an effort to stop potential stampedes in public areas. In order to detect the fall behavior of pedestrians in public places, Baidu AI was introduced in this paper to detect key skeleton points of pedestrians in a single frame sourced from surveillance videos. The ratio of human height to width and cotangent value of the direction angle of the pedestrian minimum peripheral rectangle are selected as feature vectors. Fall behavior detection model based on SVM is proposed. Experiments are designed and implemented to validate the proposed fall behavior detection model in this paper. This study can provide technical support for early warning and prevention of possible stampede accidents in public places.
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.
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