In this article, we propose the detection of crowd anomalies through the extraction of information in the form of time series from video format using a multimodal approach. Through pattern recognition algorithms and segmentation, informative measures of the number of people and image occupancy are extracted at regular intervals, which are then analyzed to obtain trends and anomalous behaviors. Specifically, through temporal decomposition and residual analysis, intervals or specific situations of unusual behaviors are identified, which can be used in decision-making and improvement of actions in sectors related to human movement such as tourism or security. The application of this methodology on the webcam of Turisme Comunitat Valenciana in the town of Morella (Comunitat Valenciana, Spain) has provided excellent results. It is shown to correctly detect specific anomalous situations and unusual overall increases during the previous weekend and during the festivities in October 2023. These results have been obtained while preserving the confidentiality of individuals at all times by using measures that maximize anonymity, without trajectory recording or person recognition.

翻译：本文提出了一种通过多模态方法从视频格式中提取时间序列信息以检测群体异常的方法。通过模式识别算法和分割技术，在固定时间间隔内提取人数和图像占用率等有信息量的度量，进而分析这些数据以获得趋势和异常行为。具体而言，通过时间分解和残差分析，识别出异常行为的时段或特定情境，这些结果可用于与人类活动相关的领域（如旅游或安全）的决策制定与行动改进。将该方法应用于巴伦西亚自治区旅游局（Comunitat Valenciana）位于莫雷利亚镇（西班牙巴伦西亚自治区）的网络摄像头数据，取得了优异成果。实验表明，该方法能正确检测到2023年10月节庆期间及前一周的特定异常情境和异常总体增长。这些结果的获取始终通过采用最大化匿名性的措施（如无轨迹记录或人员识别）来保障个体隐私。