Application of the gsp_m algorithm for the identification of behavioral patterns of people who shoplift.

This work analyzes the results of the use of the algorithm GSP_M (Generalized Sequential Patterns + memory)for processing videos of surveillance in supermarkets, in order to identify the perpetration of thefts. It describes the theoretical framework of concepts used in video surveillance systems, their architecture, and, characteristics. It justified the use of sequential pattern identification algorithms as a tool to analyze human behavior in domains of shoplifting and mathematically define some concepts used by the tool. It describes the objectives of the application of video surveillance systems in supermarkets to minimize shoplifting, explaining that this is based on the fact that, monitored people have a behavior that can be characterized by a set of sequential actions, whose analysis allows obtaining conclusions to consider a person as a suspect, generating an alert or definitely an alarm. The GSP and GSP_M algorithms are described and compared. It details the procedure defined to evaluate the performance of the algorithm GSP_M in the domain of shoplifting, and, the comparison of the results obtained with the results of the GSP application.