Asset identification using image descriptors

Asset management is a time consuming and error prone process. Information Technology (IT) personnel typically perform this task manually by visually inspecting assets to identify misplaced assets. If this process is automated and provided to IT personnel it would prove very useful in keeping track of assets in a server rack. A mobile based solution is proposed to automate this process. The asset management application on the tablet captures images of assets and searches an annotated database to identify the asset. We evaluate the matching performance and speed of asset matching using three different image feature descriptors. Methods to reduce feature extraction and matching complexity were developed. Performance and accuracy tradeoffs were studied, domain specific problems were identified, and optimizations for mobile platforms were made. The results show that the proposed methods reduce complexity of asset matching by 67% when compared to the matching process using unmodified image feature descriptors.