scholarly journals Shape derivative for obstacles in crowd motion

2021 ◽  
Vol 4 (2) ◽  
pp. 1-16
Author(s):  
Boubacar Fall ◽  
◽  
Filippo Santambrogio ◽  
Diaraf Seck ◽  
◽  
...  
Keyword(s):  
Shape Derivative ◽  
Crowd Motion

Evaluation of Crowd Motion Direction Based on Wavelet Transform

2013 ◽  
Vol 846-847 ◽  
pp. 1106-1110
Author(s):  
Guo Qing Yang ◽  
Rong Yi Cui
Keyword(s):  
Wavelet Decomposition ◽  
Estimation Method ◽  
Motion Vectors ◽  
Motion Direction ◽  
Main Research ◽  
Object A ◽  
Coordinate Form ◽  
Direction Estimation ◽  
Polar Coordinate ◽  
Crowd Motion

Taking the wavelet decomposed approximate image as the main research object, a direction estimation method for moving object was proposed in this paper. Firstly, the approximate image for the frame of the video was obtained via wavelet decomposition; and furthermore, the motion estimation on the approximate image was achieved to obtain the motion vectors. Finally, the motion vectors were described as polar coordinate form to compute the number of motion vectors in specified angles and the information entropy of the motion directions. The experiment results show that the proposed method can remove the effect of noise and the results of direction estimation are consistent with the actual motion directions.

scholarly journals Shape derivative of cost function for singular point: Evaluation by the generalized J integral

JSIAM Letters ◽  
2014 ◽  
Vol 6 (0) ◽  
pp. 29-32 ◽  
Author(s):  
Hideyuki Azegami ◽  
Kohji Ohtsuka ◽  
Masato Kimura
Keyword(s):  
Singular Point ◽  
Cost Function ◽  
Shape Derivative ◽  
J Integral ◽  
Point Evaluation

scholarly journals A System for Movement Detecting Congestion.

2014 ◽  
Vol 13 (3) ◽  
pp. 4302-4307
Author(s):  
Reeja S. R ◽  
Dr. N. P. Kavya
Keyword(s):  
Optical Flow ◽  
Optic Flow ◽  
Video Segmentation ◽  
Video Sequences ◽  
Protective Measures ◽  
Flow Computation ◽  
Congested Area ◽  
Crowd Motion ◽  
Flow Vectors ◽  
Early Information

In this paper, we present a system for tracking and provide early information of hazardous locationsin huge gatherings. It is based on optic flow estimations and detects sequences of crowd motion that are characteristic for devastating congestions. For optic flow computation, Lucas- Kanade method is employed to determine the optical flow vectors for the gathered video. Segmentation of video sequences is done and optic flow is determined for respective segments. A threshold optic flow is chosen in such a way that the tracking of congested area in video is easilydoneby comparing it with respective segment’s determined optic flow values. Finally, we present the location of crowd congestion which helps in taking further protective measures to handle unusual events.  

Derivation of shape derivative for the nanoelasticity problem

2020 ◽  
pp. 601-602
Author(s):  
Timon Rabczuk ◽  
Jeong-Hoon Song ◽  
Xiaoying Zhuang ◽  
Cosmin Anitescu
Keyword(s):  
Shape Derivative

scholarly journals Lagrangian Discretization of Crowd Motion and Linear Diffusion

2020 ◽  
Vol 58 (4) ◽  
pp. 2093-2118 ◽  
Author(s):  
Hugo Leclerc ◽  
Quentin Mérigot ◽  
Filippo Santambrogio ◽  
Federico Stra
Keyword(s):  
Linear Diffusion ◽  
Crowd Motion

scholarly journals Crowd Event Perception Based on Spatiotemporal Weber Field

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Zhou Su ◽  
Hua Wei ◽  
Sha Wei
Keyword(s):  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Interaction Force ◽  
Motion Modeling ◽  
Allocation Model ◽  
Motion Patterns ◽  
Crowd Management ◽  
Latent Dirichlet Allocation Model ◽  
Spatiotemporal Signal ◽  
Crowd Motion

Over the past decade, a wide attention has been paid to the crowd control and management in intelligent video surveillance area. Among the tasks of automatic video-based crowd management, crowd motion modeling is recognized as one of the most critical components, since it lays a crucial foundation for numerous subsequent analyses. However, it still encounters many unsolved challenges due to occlusions among pedestrians, complicated motion patterns in crowded scenarios, and so forth. Addressing these issues, we propose a novel spatiotemporal Weber field, which integrates both appearance characteristics and stimulus of crowd motion patterns, to recognize the large-scale crowd event. On the one hand, crowd motion is recognized as variations of spatiotemporal signal, and we then measure the variation based on Weber law. The result is referred to as spatiotemporal Weber variation feature. On the other hand, motivated by the achievements in crowd dynamics that crowd motion has a close relationship with interaction force, we propose a spatiotemporal Weber force feature to exploit the stimulus of crowd behaviors. Finally, we utilize the latent Dirichlet allocation model to establish the relationship between crowd events and crowd motion patterns. Experiments on PETS2009 and UMN databases demonstrate that our proposed method outperforms the previous methods for the large-scale crowd behavior perception.

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