  The escalating process of urbanization has raised concerns about incidents
arising from overcrowding, necessitating a deep understanding of large human
crowd behavior and the development of effective crowd management strategies.
This study employs computational methods to analyze real-world crowd behaviors,
emphasizing self-organizing patterns. Notably, the intersection of two streams
of individuals triggers the spontaneous emergence of striped patterns,
validated through both simulations and live human experiments. Addressing a gap
in computational methods for studying these patterns, previous research
utilized the pattern-matching technique, employing the Nelder-Mead Simplex
algorithm for fitting a two-dimensional sinusoidal function to pedestrian
coordinates. This paper advances the pattern-matching procedure by introducing
Simulated Annealing as the optimization algorithm and employing a
two-dimensional square wave for data fitting. The amalgamation of Simulated
Annealing and the square wave significantly enhances pattern fitting quality,
validated through statistical hypothesis tests. The study concludes by
outlining potential applications of this method across diverse scenarios.
