## 1 Introduction  
Nowadays, there is a growing trend of installing security and surveillance cameras, with the main goal of increasing security in public spaces, streets, offices, and private facilities [1]. Standard systems that handle traditional security cameras do not use automatic methods for detecting incidences in real-time. These surveillance systems are mainly based on operational centers that are in charge of receiving, storing, and analyzing the images and take actions when certain events happen. Operational centers needed to process images from security cameras are operated by technical professionals that play the role of visualizing agents. The job of each visualizing agent consists in constantly observing an (often large) number of images from different sources (security cameras), detecting and generating alerts in case an event of interest is observed [2]. Because of the high personnel costs, most operational centers assign to each visualizing agent a significantly large number of image sources, which could exceed their capacity. Therefore, two phenomena occur, which reduce the surveillance capabilities of the operational center. On the one hand, a degradation of the global attention or the visualizing agents, which are forced to just pay attention to a reduced number of image sources at a time, ignoring events from the rest of the sources. On the other hand, the fact that visualizing agents are human can cause them to feel bored and/or fatigue due to the monotony of the task. It frequently causes poor results. This article presents an approach applying computational intelligence to overcome the attentional problem of human visualizing agents that works in operational centers, making use of different techniques for image processing and pattern detection. A system capable of processing in real-time multiple image/video sources is proposed to help human visualizing agents in the process of identifying pedestrian movement patterns. The system focuses on harmful patterns in pedestrian movements, considering common situations for pedestrian safety as defined by the World Health Organization [3], including patterns that can cause a risk of harm (robbery, agglomeration, prowling, etc.) or have negative impacts on pedestrian safety (running, sudden direction changes, walking in forbidden areas). Furthermore, the proposed system is flexible to include/detect different pedestrian movement patterns, which are relevant for each studied scenario. The proposed system is based on a _filter and pipes_ architecture that makes it easy to exchange and evaluate different computational intelligence techniques in each stage of the process. The system is comprised of two main stages. In the first stage, image processing and filtering techniques are applied to images generated from multiple sources, extracting relevant features of images and discarding the ones that are not of interest, according to pre-loaded rules. This stage allows visualizing agents to focus their attention efficiently, on important images. The second stage is responsible for the detection of patterns, taking into account typical situations arising in surveillance and security that are worth identifying (e.g., people running, agglomerations, prowling). The system architecture and design allow extending its capabilities without significant effort, as it is easily adaptable for detection of different types of events of interest and different computational intelligence methods can be incorporated easily in the flow of the system. This article is an extended version of our conference article \"Computational intelligence for detecting pedestrian movement patterns\" [4] presented at Iberoamerican Congress on Smart Cities. The main contributions of the research reported in this article are multi-fold. First, we extend our understanding of what and why existing techniques and methods are applied in the areas of image processing and pattern detection.