AI Article Synopsis

  • The paper introduces a method for detecting key movements in crowds using a combination of direction entropy and a repulsive force network.
  • It calculates a crowd vector field and builds a weighted network to analyze the interactions between particles, which helps identify significant areas of movement.
  • Experimental results indicate that this approach effectively identifies prominent crowd motions, showcasing its potential for real-world applications.

Article Abstract

This paper proposes a method for salient crowd motion detection based on direction entropy and a repulsive force network. This work focuses on how to effectively detect salient regions in crowd movement through calculating the crowd vector field and constructing the weighted network using the repulsive force. The interaction force between two particles calculated by the repulsive force formula is used to determine the relationship between these two particles. The network node strength is used as a feature parameter to construct a two-dimensional feature matrix. Furthermore, the entropy of the velocity vector direction is calculated to describe the instability of the crowd movement. Finally, the feature matrix of the repulsive force network and direction entropy are integrated together to detect the salient crowd motion. Experimental results and comparison show that the proposed method can efficiently detect the salient crowd motion.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515092PMC
http://dx.doi.org/10.3390/e21060608DOI Listing

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