OSCOR: an orientation sensor data correction system for mobile generated contents

In addition to positioning data, other sensor information -- such as orientation data, have become a useful and powerful contextual feature. Such auxiliary information can facilitate higher-level semantic description inferences in many multimedia applications, e.g., video tagging and video summarization. However, sensor data collected from current mobile devices is often not accurate enough for upstream multimedia analysis. An effective orientation data correction system for mobile multimedia content has been an elusive goal so far. Here we present a system, termed Oscor, which aims to improve the accuracy of noisy orientation sensor measurements generated by mobile devices during image and video recording. We provide a user-friendly camera interface to facilitate the gathering of additional information, which enables the correction process on the server-side. Geographic field-of-view (FOV) visualizations based on the original and corrected sensor data help users understand the corrected contextual information and how the erroneous data possibly may affect further processes.