<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In the field of intelligent crowd video analysis, the prediction of abnormal events in dense crowds is a well-known and challenging problem. By analysing crowd particle collisions and characteristics of individuals in a crowd to follow the general trend of motion, a purpose-driven lattice Boltzmann model (LBM) is proposed. The collision effect in the proposed method is measured according to the variation in crowd particle numbers in the image nodes; characteristics of the crowd following a general trend are incorporated by adjusting the particle directions. The model predicts dense crowd abnormal events in different intervals through iterations of simultaneous streaming and collision steps. Few initial frames of a video are needed to initialize the proposed model and no training procedure is required. Experimental results show that our purpose-driven LBM performs better than most state-of-the-art methods.</p></abstract>

In the field of intelligent crowd video analysis, the prediction of abnormal events in dense crowds is a well-known and challenging problem. By analysing crowd particle collisions and characteristics of individuals in a crowd to follow the general trend of motion, a purpose-driven lattice Boltzmann model (LBM) is proposed. The collision effect in the proposed method is measured according to the variation in crowd particle numbers in the image nodes; characteristics of the crowd following a general trend are incorporated by adjusting the particle directions. The model predicts dense crowd abnormal events in different intervals through iterations of simultaneous streaming and collision steps. Few initial frames of a video are needed to initialize the proposed model and no training procedure is required. Experimental results show that our purpose-driven LBM performs better than most state-of-the-art methods.

Abnormal Prediction of Dense Crowd Videos by a Purpose–Driven Lattice Boltzmann Model

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

In the field of intelligent crowd video analysis, the prediction of abnormal events in dense crowds is a well-known and challenging problem. By analysing crowd...

Abnormal Prediction of Dense Crowd Videos by a Purpose–Driven Lattice Boltzmann Model

Online veröffentlicht: 04. Mai 2017

Seitenbereich: 181 - 194

Eingereicht: 31. Jan. 2016

Akzeptiert: 10. Sept. 2016

DOI: https://doi.org/10.1515/amcs-2017-0013

Schlüsselwörter
video surveillance, crowd analysis, abnormal events, lattice Boltzmann model, purpose-driven strategy

© by Ye Tao

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Abnormal Prediction of Dense Crowd Videos by a Purpose–Driven Lattice Boltzmann Model

Online veröffentlicht: 04. Mai 2017

Seitenbereich: 181 - 194

Eingereicht: 31. Jan. 2016

Akzeptiert: 10. Sept. 2016

DOI: https://doi.org/10.1515/amcs-2017-0013

Schlüsselwörtervideo surveillance, crowd analysis, abnormal events, lattice Boltzmann model, purpose-driven strategy

© by Ye Tao

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
video surveillance, crowd analysis, abnormal events, lattice Boltzmann model, purpose-driven strategy