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Paper details
Number 1 - March 2017
Volume 27 - 2017
Abnormal prediction of dense crowd videos by a purpose-driven lattice Boltzmann model
Yiran Xue, Peng Liu, Ye Tao, Xianglong Tang
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.
Keywords
video surveillance, crowd analysis, abnormal events, lattice Boltzmann model, purpose-driven strategy