# Multimodal Video Analysis for Crowd Anomaly Detection Using Open Access Tourism Cameras
## 2 Related Work  
This section presents a synthesis of recent research on anomaly detection, object detection in public safety, and crowd analysis. It encompasses a range of methodologies, including advanced machine learning techniques for time series analysis, video surveillance, and real-time occupancy monitoring. Prominent approaches, such as Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs), and YOLO (You Only Look Once) models, are examined for their effectiveness in anomaly detection, object tracking, and crowd behavior analysis. The studies also address key challenges related to enhancing detection accuracy, managing spatio-temporal data, and optimizing efficiency in complex real-world scenarios. A summary of the principal contributions in these areas follows. In [4], a literature review is conducted, and a taxonomy for anomaly detection in time series is proposed. This taxonomy is distinguished according to the type of anomaly detected, which can be point anomalies, contextual anomalies, or collective anomalies; the characteristics of the series, i.e., whether it deals with univariate or multivariate series; the context of the data, i.e., considering the spatio-temporal characteristics of the series in the analysis; and the methodology used, either through machine learning or statistical methods. Additionally, it also provides a review of the challenges that may be encountered when using these techniques, such as defining the normality of the data, the under-representation of anomalies in datasets, or the need to differentiate anomalies from possible noise present in the series. In [5], a system based on the Spectral Residual (SR) algorithm and CNNs is proposed, allowing online anomaly detection in business metric time series. In [6], a system for real-time anomaly detection is proposed, capable of detecting different types of anomalies due to the integration of various anomaly detection and prediction models. In [7], a model for unsupervised anomaly detection based on GANs is proposed, with both the actors and critics of these networks based on LSTM recurrent networks, where series reconstruction allows anomaly detection. In [8], a model based on Transformers for unsupervised anomaly detection is proposed. In this case, a version of Transformer with modified attention mechanism (Anomaly-Attention) is implemented, aiming to overcome the limitations of these in the task of anomaly detection. In [9], a methodology for real-time anomaly detection in video is introduced. This approach utilizes feature maps, extracted from the energy of each frame, enabling the detection of object movement speed. Consequently, frames where the energy undergoes abrupt changes are identified as anomalous. In [10], a methodology for detecting suspicious behaviors in video is presented. Through object detection and tracking algorithms, the spatio-temporal characteristics of individuals in the video are extracted. Subsequently, the series obtained from these characteristics are classified based on whether they exhibit anomalous behaviors or not. In [11], a novel approach for abandoned object detection is introduced. This approach uses a dual background model that adapts to scene changes, an enhanced Pixel-based Finite State Machine (PFSM) with occlusion handling, and the SAO-FPN network for improved small-object feature extraction. Integrating the SODHead decoupling head and self-attention further emphasizes occluded features. Tests on datasets like ABODA and VisDrone2019 show significant accuracy improvements, with a mean detection accuracy of 91.1%, outperforming other advanced methods in recovery rate and accuracy. In [12], a super-resolution method for enhancing public safety through the analysis of surveillance videos is presented, integrating object detection, key frame selection, and super-resolution reconstruction.