Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
6-2016
Abstract
Understanding one's group context in indoor spaces is useful for many reasons - e.g., at a shopping mall, knowing a customer's group context can help in offering context-specific incentives, or estimating taxi demand for customers exiting the mall. Group detection and monitoring using WiFi-based indoor location traces fails when users are invisible (either because they don't carry smartphones, or because their WiFi is turned OFF) or when location tracking is inaccurate. In this paper, we propose a multi-modal group detection system that fuses two independent modes: video and WiFi, for detecting groups with low latency and high accuracy. We present preliminary results from a micro-study with 20 group episodes and report an overall precision of 0.81 and recall of 0.9, an improvement of over ≈20% over WiFi-based group detection.
Keywords
Group Monitoring, Multi-Modal Sensing, Sensor Fusion
Discipline
Software Engineering | Technology and Innovation
Research Areas
Software and Cyber-Physical Systems
Publication
WPA '16: Proceedings of the 2016 Workshop on Physical Analytics, Singapore, 2016 June 26
First Page
49
Last Page
54
ISBN
9781450343282
Identifier
10.1145/2935651.2935659
Publisher
ACM
City or Country
New York
Citation
JAYARAJAH, Kasthuri; LANTRA, Zaman; and MISRA, Archan.
Fusing WiFi and video sensing for accurate group detection in indoor spaces. (2016). WPA '16: Proceedings of the 2016 Workshop on Physical Analytics, Singapore, 2016 June 26. 49-54.
Available at: https://ink.library.smu.edu.sg/sis_research/3636
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/2935651.2935659