TY - GEN
T1 - Fusing WiFi and video sensing for accurate group detection in indoor spaces
AU - Jayarajah, Kasthuri
AU - Lantra, Zaman
AU - Misra, Archan
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/6/26
Y1 - 2016/6/26
N2 - 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.
AB - 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.
KW - Group Monitoring;
KW - Multi-Modal Sensing;
KW - Sensor Fusion
UR - http://www.scopus.com/inward/record.url?scp=84979942203&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84979942203&partnerID=8YFLogxK
U2 - 10.1145/2935651.2935659
DO - 10.1145/2935651.2935659
M3 - Conference contribution
AN - SCOPUS:84979942203
T3 - WPA 2016 - Proceedings of the 3rd International Workshop on Physical Analytics, co-located with MobiSys 2016
SP - 49
EP - 54
BT - WPA 2016 - Proceedings of the 3rd International Workshop on Physical Analytics, co-located with MobiSys 2016
PB - Association for Computing Machinery, Inc
T2 - 3rd International Workshop on Physical Analytics, WPA 2016
Y2 - 26 June 2016
ER -