TY - GEN
T1 - Demo abstract
T2 - 12th ACM Conference on Embedded Networked Sensor Systems, SenSys 2014
AU - Jayarajah, Kasthuri
AU - Sen, Rijurekha
AU - Lee, Youngki
AU - Nayak, Shriguru
AU - Misra, Archan
AU - Balan, Rajesh
PY - 2014/11/3
Y1 - 2014/11/3
N2 - Detecting the group context of an individual (i.e., whether an individual is alone or part of a group) in crowded public spaces, such as shopping malls, is an important goal with many practical applications. However, in crowded indoor spaces, understanding the group-dependent movement behavior is a non-trivial problem as: (1) detecting groups is hard as the density ensures that at any location, a large number of people are moving together, (2) location tracking in many real-world venues is either absent or not very accurate, and (3) indoor mobility models that take into account group attributes (such as group size) are rare. In this paper, we first introduce GruMon, a platform for near real-time group monitoring in dense, public spaces, and then demonstrate how the movement & residency properties of individuals are significantly affected when they are in groups.
AB - Detecting the group context of an individual (i.e., whether an individual is alone or part of a group) in crowded public spaces, such as shopping malls, is an important goal with many practical applications. However, in crowded indoor spaces, understanding the group-dependent movement behavior is a non-trivial problem as: (1) detecting groups is hard as the density ensures that at any location, a large number of people are moving together, (2) location tracking in many real-world venues is either absent or not very accurate, and (3) indoor mobility models that take into account group attributes (such as group size) are rare. In this paper, we first introduce GruMon, a platform for near real-time group monitoring in dense, public spaces, and then demonstrate how the movement & residency properties of individuals are significantly affected when they are in groups.
UR - http://www.scopus.com/inward/record.url?scp=84914145148&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84914145148&partnerID=8YFLogxK
U2 - 10.1145/2668332.2668375
DO - 10.1145/2668332.2668375
M3 - Conference contribution
AN - SCOPUS:84914145148
T3 - SenSys 2014 - Proceedings of the 12th ACM Conference on Embedded Networked Sensor Systems
SP - 318
EP - 319
BT - SenSys 2014 - Proceedings of the 12th ACM Conference on Embedded Networked Sensor Systems
PB - Association for Computing Machinery
Y2 - 3 November 2014 through 6 November 2014
ER -