Abstract
In the computer vision field, understanding human dynamics is not only a great challenge but also very meaningful work, which plays an indispensable role in public safety. Despite the complexity of human dynamics, physicists have found that pedestrian motion in a crowd is governed by some internal rules, which can be formulated as a motion model, and an effective model is of great importance for understanding and reconstructing human dynamics in various scenes. In this paper, we revisit the related research in social psychology and propose a two-part motion model based on the shortest path principle. One part of the model seeks the origin and destination of a pedestrian, and the other part generates the movement path of the pedestrian. With the proposed motion model, we simulated the movement behavior of pedestrians and classified them into various patterns. We next reconstructed the crowd motions in a real-world scene. In addition, to evaluate the effectiveness of the model in crowd motion simulations, we created a new indicator to quantitatively measure the correlation between two groups of crowd motion trajectories. The experimental results show that our motion model outperformed the state-of-the-art model in the above applications.
Highlights
Crowd motion is a common phenomenon in human society, which often appears in train stations, shopping malls, street intersections and other mass events
Inspired by the model of dynamic pedestrian agents (MDA) model, we propose a new model based on the shortest path principle to describe pedestrian motion in a crowd
We revisited the research results of sociologists to summarize the shortest path principle, and proposed a new motion model to simulate the moving of pedestrians in crowded scenes
Summary
Crowd motion is a common phenomenon in human society, which often appears in train stations, shopping malls, street intersections and other mass events. The collective wills of pedestrians reinforce the shortest path principle. Based on the shortest path principle, we propose a two-part motion model to describe these basic elements of pedestrian motions. Combined with the connectivity between trajectories, we propose a generalized path likelihood to estimate the probability of the trajectory reaching each candidate pair of origin and destination. We verified the effectiveness of the proposed motion model in the following applications: a pedestrian motion simulation, motion pattern classification and traffic flow statistics, and crowd motion description and simulation. (2) Based on the sociological findings, we integrated the shortest path principle into the proposed motion model, making our motion model highly consistent with the behavioral decision-making of pedestrians. This strategy takes advantage of the spatial and temporal information of the trajectories. (4) To assess the simulation of crowding behavior, we propose an indicator to quantitatively measure the correlation between two groups of crowd motion trajectories
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