Video based mobile location search with large set of SIFT points in cloud

Authors:
Zhi Ye;Xin Chen;Zhu Li
Affiliations:
The Hong Kong Polytechnic University, Hong Kong, Hong Kong;The Hong Kong Polytechnic University, Hong Kong, Hong Kong;The Hong Kong Polytechnic University, Hong Kong, Hong Kong
Venue:
Proceedings of the 2010 ACM multimedia workshop on Mobile cloud media computing
Year:
2010

Citing 6
Cited 3

A taxonomy of indoor and outdoor positioning techniques for mobile location services

ACM SIGecom Exchanges - Mobile commerce
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
A Performance Evaluation of Local Descriptors

IEEE Transactions on Pattern Analysis and Machine Intelligence
A 3-dimensional sift descriptor and its application to action recognition

Proceedings of the 15th international conference on Multimedia
SIFT-Bag kernel for video event analysis

MM '08 Proceedings of the 16th ACM international conference on Multimedia
PCA-SIFT: a more distinctive representation for local image descriptors

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition

SENST*: approaches for reducing the energy consumption of smartphone-based context recognition

CONTEXT'11 Proceedings of the 7th international and interdisciplinary conference on Modeling and using context
Constraint-optimized keypoint inhibition/insertion attack: security threat to scale-space image feature extraction

Proceedings of the 20th ACM international conference on Multimedia
Exact and easy guidance with visual navigation situation for mobile user

Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service

Quantified Score

Hi-index	0.00

Visualization

Abstract

In densely populated cities like Hong Kong, GPS alone is not sufficient in providing rich location based information services. In this work, we provide a mobile location search service by allowing users shooting a short video clip about the surrounding buildings and the Cloud will return a tagged image with a summary of the location and services available. The key technical challenges are the robustness of the SIFT based object matching in video sequences, and the computational complexity associated with the large scale of the repository. We solved this by spatio-temporal pruning of SIFT points in the video repository, and PCA projection and indexing of SIFT points in Cloud. Simulation results demonstrated the robustness of the PCAed and indexed SIFT points in building identification and overall effectiveness of the proposed solution on a small scale trial. Tagging and integration with large scale video repositories like YouTube, Tudou are underway with more interesting applications.