CROWD SOURCING ANALYSIS

This paper presents a comprehensive real-time crowdsourcing-based system for detecting potentially dangerous crowd conditions at large public gatherings. By integrating surveillance infrastructure, mobile sensor networks, wearable device data, and direct participant reports, the system addresses critical blind spots in traditional monitoring approaches. The multi-source architecture enables earlier detection of emerging stampede risks and supports rapid response by event administrators and emergency personnel. The proposed system uses consensus between independent streams of information, unlike existing approaches that rely on a single modality of data. This greatly reduces false alarm rates while still maintaining high detection sensitivity. The system is designed to shorten attacker dwell time in crowd safety scenarios and allow proactive interventions rather than reactive emergency response. The increasing scale and complexity of public events necessitate the use of monitoring systems to ensure public safety. This study provides a structure for the practical deployment of these monitoring systems within real-world event management operations.

[1]     K. Helbing, I. Farkas, and T. Vicsek, “Simulating dynamical features of escape panic,” Nature, vol. 407, pp. 487–490, 2000. [2]     J. Fruin, “Pedestrian Planning and Design,” Metropolitan Association of Planning Officials, New York, 1971. [3]     C. Zhang, P. Dollar, C. Seitz, and S. Hinds, “Crowd detection with deep convolutional neural network,” in IEEE Int. Conf. Image Process. (ICIP), 2014. [4]     M. Kratz and K. Nishino, “Tracking pedestrians with 3D body shape models,” in IEEE Int. Conf. Computer Vision (ICCV), 2009. [5]     A. Mahadevan, S. Li, and S. Bhattacharyya, “Anomalous event detection in videos using spatiotemporal autoencoder,” in Adv. Neural Inf. Process. Syst. (NIPS), 2010. [6]     J. Kim, C. Grauman, and A. Bobick, “Large displacement 3D object tracking with occlusion handling,” in IEEE Int. Conf. Computer Vision (ICCV), 2015. [7]     O. Masoud and N. Papanikolopoulos, “A novel method for tracking and counting pedestrians in real-time using a single camera,” IEEE Trans. Veh. Technol., vol. 50, no. 5, pp. 1323–1335, 2001. [8]     S. Ryan, “Detecting anomalous crowd behavior using spatiotemporal features,” Master’s thesis, University of Central Florida, 2014. [9]     R. Mehran, A. Oyama, and M. Shah, “Abnormal crowd behavior detection using social force model,” in IEEE Conf. Computer Vision Pattern Recognit. (CVPR), 2009. [10]   V. Kraemer, L. Martin, and P. Suter, “Emergency response protocols for crowd management at large-scale events,” in Proc. Int. Conf. Emergency Manag., 2018.