Dr. Kyu-Han Kim Profile

Dr. Kyu-Han Kim

at Deutsche Telekom Labs

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Publications (1)

Proceedings Article | 8 September 2010 Paper

Low-cost asset tracking using location-aware camera phones

David Chen, Sam Tsai, Kyu-Han Kim, Cheng-Hsin Hsu, Jatinder Pal Singh, Bernd Girod

Proceedings Volume 7798, 77980R (2010) https://doi.org/10.1117/12.862426

KEYWORDS: Spine, Image segmentation, Databases, Sensors, Visualization, Image analysis, Cameras, Optical character recognition, Feature extraction, Detection and tracking algorithms

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Maintaining an accurate and up-to-date inventory of one's assets is a labor-intensive, tedious, and costly operation. To ease this difficult but important task, we design and implement a mobile asset tracking system for automatically generating an inventory by snapping photos of the assets with a smartphone. Since smartphones are becoming ubiquitous, construction and deployment of our inventory management solution is simple and costeffective. Automatic asset recognition is achieved by first segmenting individual assets out of the query photo and then performing bag-of-visual-features (BoVF) image matching on the segmented regions. The smartphone's sensor readings, such as digital compass and accelerometer measurements, can be used to determine the location of each asset, and this location information is stored in the inventory for each recognized asset. As a special case study, we demonstrate a mobile book tracking system, where users snap photos of books stacked on bookshelves to generate a location-aware book inventory. It is shown that segmenting the book spines is very important for accurate feature-based image matching into a database of book spines. Segmentation also provides the exact orientation of each book spine, so more discriminative upright local features can be employed for improved recognition. This system's mobile client has been implemented for smartphones running the Symbian or Android operating systems. The client enables a user to snap a picture of a bookshelf and to subsequently view the recognized spines in the smartphone's viewfinder. Two different pose estimates, one from BoVF geometric matching and the other from segmentation boundaries, are both utilized to accurately draw the boundary of each spine in the viewfinder for easy visualization. The BoVF representation also allows matching each photo of a bookshelf rack against a photo of the entire bookshelf, and the resulting feature matches are used in conjunction with the smartphone's orientation sensors to determine the exact location of each book.

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