Crowd-Motion Analysis for Disaster Prediction: A Review of Computer Vision Approaches

DOI

10.2991/978-94-6239-789-7_37How to use a DOI?

Keywords

Crowd Disaster Prediction; Abnormal Crowd Behavior Detection; Multi-Camera Surveillance

Abstract

Crowd disasters in large public gatherings pose a major challenge to public safety, particularly in dense and dynamically changing environments where manual surveillance becomes inefficient, error-prone, and difficult to scale. Computer vision has emerged as a promising tool for automated crowd understanding, enabling the analysis of motion pat-terns, abnormal behavior, and scene dynamics from surveillance videos. This paper presents a comprehensive review of computer vision-based methods for crowd-motion behavior analysis with emphasis on their relevance to crowd disaster prediction and prevention. The survey examines key research directions, including abnormal behavior detection, crowd-motion pattern analysis, and benchmark video datasets commonly used for evaluation. It further analyzes the strengths and limitations of existing approaches in the context of real-world crowded scenes, where challenges such as severe occlusion, limited field of view, track fragmentation, and high crowd density significantly affect performance. In addition, the paper reviews the characteristics of available datasets and highlights the limited availability of multi-camera benchmark data for realistic crowd analysis. The study identifies important research gaps in real-time crowd disaster prediction and argues that more robust, scalable, and multi-view vision frameworks are needed to support intelligent surveillance in high-density public events.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Tanu Gupta
AU  - Mridula Singh
AU  - Brij Mohan Singh
AU  - Ashish Kumar
PY  - 2026
DA  - 2026/10/08
TI  - Crowd-Motion Analysis for Disaster Prediction: A Review of Computer Vision Approaches
BT  - Proceedings of theInternational Conference on Innovation and Practices in Artificial Intelligence (ICIPAI 2026)
PB  - Atlantis Press
SP  - 552
EP  - 574
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-789-7_37
DO  - 10.2991/978-94-6239-789-7_37
ID  - Gupta2026
ER  -