Abnormal prediction of dense crowd videos by a purpose-driven lattice Boltzmann model

Yiran Xue; Peng Liu; Ye Tao; Xianglong Tang

International Journal of Applied Mathematics and Computer Science (2017)

  • Volume: 27, Issue: 1, page 181-194
  • ISSN: 1641-876X

Abstract

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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.

How to cite

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Yiran Xue, et al. "Abnormal prediction of dense crowd videos by a purpose-driven lattice Boltzmann model." International Journal of Applied Mathematics and Computer Science 27.1 (2017): 181-194. <http://eudml.org/doc/288090>.

@article{YiranXue2017,
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.},
author = {Yiran Xue, Peng Liu, Ye Tao, Xianglong Tang},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {video surveillance; crowd analysis; abnormal events; lattice Boltzmann model; purpose-driven strategy},
language = {eng},
number = {1},
pages = {181-194},
title = {Abnormal prediction of dense crowd videos by a purpose-driven lattice Boltzmann model},
url = {http://eudml.org/doc/288090},
volume = {27},
year = {2017},
}

TY - JOUR
AU - Yiran Xue
AU - Peng Liu
AU - Ye Tao
AU - Xianglong Tang
TI - Abnormal prediction of dense crowd videos by a purpose-driven lattice Boltzmann model
JO - International Journal of Applied Mathematics and Computer Science
PY - 2017
VL - 27
IS - 1
SP - 181
EP - 194
AB - 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.
LA - eng
KW - video surveillance; crowd analysis; abnormal events; lattice Boltzmann model; purpose-driven strategy
UR - http://eudml.org/doc/288090
ER -

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