MOSRO: Enabling mobile sensing for real-scene objects with grid based structured output learning

Abstract

Visual objects in mobile photos are usually captured in uncontrolled conditions, such as various viewpoints, positions, scales, and background clutter. In this paper, therefore, we developed a MObile Sensing framework for robust Real-scene Object recognition and localization (MOSRO). By extending the conventional structured output learning with the proposed grid based representation as the output structure, MOSRO is not only able to locate the visual objects precisely but also achieve real-time performances. The experimental results showed that the proposed framework outperforms the state-of-the-art methods on public real-scene image datasets. Further, to demonstrate its effectiveness for practical applications, the proposed MOSRO framework was implemented on Android mobile platforms as a prototype system for sensing various business signs on the street and instantly retrieving relevant information of the recognized businesses. © 2014 Springer International Publishing.