A Fast, Multi-Camera, and Intelligent System for Exact Stampede Detection in Large Crowds

Abstract

With the increasing population, events with large crowds also increased. It often leads to uncontrolled stampede situations, causing several deaths. Deployment of intelligent systems with the quick alert feature may reduce the impact of stampedes. Researchers utilized traditional deep learning models on a centralized server for stampede detection. These models have high time, computational complexity, unaddressed public privacy concerns, and misclassification due to less inter-class variance. We thus propose a low-cost, fast, and intelligent system named StampSys, for accurate stampede detection over large crowds in multi-camera environment. To address complexity and privacy issues, we introduce a novel light-weight multi-modal federated learning setup. We include a novel multi-label fuzzy classifier to improve the global decision. We create a new annotated dataset, entitled CrowdStampede with 6K images. The experimentation results show that our system accurately classifies stampede situations on our dataset.