Real-time Mobile Visual Object Recognition in Real Scene

The popularization of mobile devices and the advancement of wearable devices make the augmented reality (AR) scenarios become feasible. However, the success of AR applications relies on a key technique, real-time visual object recognition in real scene. Therefore, in our dissertation, we developed a framework called SpAtialized Grid based structured learning for Real-scene Object recognition (SAGRO). The proposed SAGRO is not only able to locate the visual objects precisely but also achieves real-time performances. Based on the techniques of mobile visual object recognition, we presented two applications to improve user experiences in their daily life. First, we proposed a commercial item retrieval and recommendation system, UbiShop, on mobile phones, whereby users can timely get the related information of interesting commercial items by taking pictures of them. Users can also obtain recommendations on visually similar commercial items to help their buying selections. Moreover, observing the fact that more than 63 percent of the drivers in the United States in 2013 have been led astray because of receiving confusing GPS driving instructions, we presented a more intuitive driving instruction, iNavi, by detecting interesting regions from the sight of vehicle drivers to help them quickly and correctly recognize the turning points.