E fficient Summarization From Multiple Georeferenced User-Generated Videos
Abstract: The rapid developments in camera technology and mobile devices bring a flourish of user-generated videos with rich geographic metadata, which can be great information resources for prospective tourists to preview a place of interest. In a video retrieval system, a simple query will return many videos. To provide users with a convenient way to explore videos, we generate summarization from multiple georeferenced videos, which is composed of segments from different input videos but complementing each other to preserve the regions of interest (ROIs) among the original inputs. Different from conventional ROI detection techniques which extract objects from a single video with dataintensivecomputing, the proposed strategy solely leverages the geographic metadata among multiple videos (we term the ROI as Geographic-ROI/GROI). A Gaussian-based model is proposed to formulate the capture intention distribution in geo-space for each video-frame. By selecting such characteristics from keyframes in each video, we successfully detect the GROIs in a few milliseconds with average error distances within a few meters. Based on this, we select representative video segments capturing popular GROIs and compose them in a coherent manner as a final summarization. To speed up the processing, we represent videos according to their geographic characteristics and actively select their representative pieces ( geo-keys). The highest quality videos among the local scope (key- neighborhood) for geo-keys are added to the summary with an
appropriate travel route determined by their spatial consistency. Experiments indicate our summarization approach out-performs the state-of-the-arts by preserving the original GROIs with better accuracy and within less time.