Multimodal video analysis for crowd anomaly detection using open access tourism cameras

TL;DR

This paper presents a multimodal video analysis method for crowd anomaly detection using open access tourism cameras, extracting time series data to identify unusual behaviors without compromising individual privacy.

Contribution

The study introduces a novel multimodal approach combining pattern recognition and temporal analysis for crowd anomaly detection using publicly available tourism webcams.

Findings

Accurately detected specific crowd anomalies during events and weekends.

Effectively identified unusual increases in crowd density.

Maintained individual anonymity while analyzing video data.

Abstract

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.