Work

Ph.D. Forum: A Study on Real-time Crowdedness Sensing and Pedestrian Tracking in Multi-environment

Year: 2024

Type: conference-paper

Abstract: As urban areas continue to expand and populations grow, cities increasingly face challenges related to crowd management. Dense crowds can significantly impact urban traffic, safety, and management, while also creating discomfort in overcrowded spaces. This study investigates how multimodal ubiquitous sensing can be used to create real-time crowdedness sensing and pedestrian tracking in different scenarios. This study proposes new computer vision and wireless signal-based methods for deployment, experiment, evaluation and comparison in open space, semi-open space and closed space, respectively. This research aims to offer reference and guidance for applying crowdedness sensing technologies in various scenarios, which will help in better crowd management and the data sensing of smart cities.

Source: ACM Conference on Embedded Networked Sensor Systems (SenSys)

DOI: 10.1145/3666025.3699666

Author: Wenhao Huang

Institution: Keio University Shonan Fujisawa

Language: English

Topic: Video Surveillance and Tracking Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: bronze

Funder: Japan Society for the Promotion of Science