Publication Type

Conference Proceeding Article

Version

Postprint

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

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

https://doi.org/10.1145/2935651.2935659

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