TY - GEN
T1 - Low bandwidth offload for mobile AR
AU - Jain, Puneet
AU - Manweiler, Justin
AU - Choudhury, Romit Roy
N1 - We sincerely thank our many volunteers, Dr. Ganesh Ananthanarayanan our shepherd, as well the anonymous reviewers for their invaluable feedback. We are also grateful to Intel, Google, and NSF for partially funding this research through the grant NSF 1430064.
PY - 2016/12/6
Y1 - 2016/12/6
N2 - Environmental fingerprinting has been proposed as a key enabler to immersive, highly contextualized mobile computing applications, especially augmented reality. While fingerprints can be constructed in many domains (e.g., wireless RF, magnetic field, and motion patterns), visual fingerprinting is especially appealing due to the inherent heterogeneity in many indoor spaces. This visual diversity, however, is also its Achilles' heel - matching a unique visual signature against a database of millions requires either impractical computation for a mobile device, or to upload large quantities of visual data for cloud offload. Further, most visual "features" tend to be low entropy - e.g., homogeneous repetitions of floor and ceiling tiles. Our system VisualPrint, proposes a means to offload only the most distinctive visual data, that is, only those visual signatures which stand a good chance to yield a unique match. VisualPrint enables cloud-offloaded visual fingerprinting with efficacy comparable to using whole images, but with an order reduction in network transfer.
AB - Environmental fingerprinting has been proposed as a key enabler to immersive, highly contextualized mobile computing applications, especially augmented reality. While fingerprints can be constructed in many domains (e.g., wireless RF, magnetic field, and motion patterns), visual fingerprinting is especially appealing due to the inherent heterogeneity in many indoor spaces. This visual diversity, however, is also its Achilles' heel - matching a unique visual signature against a database of millions requires either impractical computation for a mobile device, or to upload large quantities of visual data for cloud offload. Further, most visual "features" tend to be low entropy - e.g., homogeneous repetitions of floor and ceiling tiles. Our system VisualPrint, proposes a means to offload only the most distinctive visual data, that is, only those visual signatures which stand a good chance to yield a unique match. VisualPrint enables cloud-offloaded visual fingerprinting with efficacy comparable to using whole images, but with an order reduction in network transfer.
KW - Augmented reality
KW - Bandwidth
KW - Latency
KW - Offloading
UR - https://www.scopus.com/pages/publications/85009830099
UR - https://www.scopus.com/pages/publications/85009830099#tab=citedBy
U2 - 10.1145/2999572.2999587
DO - 10.1145/2999572.2999587
M3 - Conference contribution
AN - SCOPUS:85009830099
T3 - CoNEXT 2016 - Proceedings of the 12th International Conference on Emerging Networking EXperiments and Technologies
SP - 237
EP - 251
BT - CoNEXT 2016 - Proceedings of the 12th International Conference on Emerging Networking EXperiments and Technologies
PB - Association for Computing Machinery
T2 - 12th ACM Conference on Emerging Networking Experiments and Technologies, ACM CoNEXT 2016
Y2 - 12 December 2016 through 15 December 2016
ER -