Modeling Pedestrian Motion in Crowded Scenes Based on the Shortest Path Principle

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.

A Perceptually-Validated Metric for Crowd Trajectory Quality Evaluation

Simulating crowds requires controlling a very large number of trajectories and is usually performed using crowd motion algorithms for which appropriate parameter values need to be found. The study of the relation between parametric values for simulation techniques and the quality of the resulting trajectories has been studied either through perceptual experiments or by comparison with real crowd trajectories. In this paper, we integrate both strategies. A quality metric, QF, is proposed to abstract from reference data while capturing the most salient features that affect the perception of trajectory realism. QF weights and combines cost functions that are based on several individual, local and global properties of trajectories. These trajectory features are selected from the literature and from interviews with experts. To validate the capacity of QF to capture perceived trajectory quality, we conduct an online experiment that demonstrates the high agreement between the automatic quality score and non-expert users. To further demonstrate the usefulness of QF, we use it in a data-free parameter tuning application able to tune any parametric microscopic crowd simulation model that outputs independent trajectories for characters. The learnt parameters for the tuned crowd motion model maintain the influence of the reference data which was used to weight the terms of QF.

From HRI to CRI: Crowd Robot Interaction—Understanding the Effect of Robots on Crowd Motion

How does the presence of a robot affect pedestrians and crowd dynamics, and does this influence vary across robot type? In this paper, we took the first step towards answering this question by performing a crowd-robot gate-crossing experiment. The study involved 28 participants and two distinct robot representatives: A smart wheelchair and a Pepper humanoid robot. Collected data includes: video recordings; robot and participant trajectories; and participants’ responses to post-interaction questionnaires. Quantitative analysis on the trajectories suggests the robot affects crowd dynamics in terms of trajectory regularity and interaction complexity. Qualitative results indicate that pedestrians tend to be more conservative and follow “social rules” while passing a wheelchair compared to a humanoid robot. These insights can be used to design a social navigation strategy that allows more natural interaction by considering the robot effect on the crowd dynamics.

Optical Flow-Based Weighted Magnitude and Direction Histograms for the Detection of Abnormal Visual Events Using Combined Classifier

Movement information of persons is a very vital feature for abnormality detection in crowded scenes. In this paper, a new method for detection of crowd escape event in video surveillance system is proposed. The proposed method detects abnormalities based on crowd motion pattern, considering both crowd motion magnitude and direction. Motion features are described by weighted-oriented histogram of optical flow magnitude (WOHOFM) and weighted-oriented histogram of optical flow direction (WOHOFD), which describes local motion pattern. The proposed method uses semi-supervised learning approach using combined classifier (KNN and K-Means) framework to detect abnormalities in motion pattern. The authors validate the effectiveness of the proposed approach on publicly available UMN, PETS2009, and Avanue datasets consisting of events like gathering, splitting, and running. The technique reported here has been found to outperform the recent findings reported in the literature.

Disease contagion models coupled to crowd motion and mesh-free simulation

Modeling and simulation of disease spreading in pedestrian crowds have recently become a topic of increasing relevance. In this paper, we consider the influence of the crowd motion in a complex dynamical environment on the course of infection of the pedestrians. To model the pedestrian dynamics, we consider a kinetic equation for multi-group pedestrian flow based on a social force model coupled with an Eikonal equation. This model is coupled with a non-local SEIS contagion model for disease spread, where besides the description of local contacts, the influence of contact times has also been modeled. Hydrodynamic approximations of the coupled system are derived. Finally, simulations of the hydrodynamic model are carried out using a mesh-free particle method. Different numerical test cases are investigated, including uni- and bi-directional flow in a passage with and without obstacles.

Modeling of crowds in regions with moving obstacles

<p style='text-indent:20px;'>We present a model of crowd motion in regions with moving obstacles, which is based on the notion of measure sweeping process. The obstacle is modeled by a set-valued map, whose values are complements to <inline-formula><tex-math id="M1">\begin{document}$ r $\end{document}</tex-math></inline-formula>-prox-regular sets. The crowd motion obeys a nonlinear transport equation outside the obstacle and a normal cone condition (similar to that of the classical sweeping processes theory) on the boundary. We prove the well-posedness of the model, give an application to environment optimization problems, and provide some results of numerical computations.</p>

Crowd Motion Editing Based on Mesh Deformation

Computer simulation is a significant technology on making great scenes of crowd in the film industry. However, current animation making process of crowd motion requires large manual operations which are time-consuming and inconvenient. To solve the above problem, this paper presents an editing method on the basis of mesh deformation that can rapidly and intuitively edit crowd movement trajectories from the perspective of time and space. The method is applied to directly generate and adjust the crowd movement as well as avoid the crash between crowd and obstacles. As for collisions within the crowd that come along with path modification problem, a time-based solution is put forward to avoid this situation by retaining relative positions of individuals. Moreover, an experiment based on a real venue was performed and the result indicates that the proposed method can not only simplify the editing operations but also improve the efficiency of crowd motion editing.