We investigate the problem of stitching timely synchronized video streams captured by freely moving devices. Recently, it was shown that using frame-to-frame correlation can greatly enhance the efficiency and effectiveness of video stitching algorithms [19]. In this paper, we address some of the shortcomings in [19], namely the simple blending approach, causing almost a third of stitching errors and the fact that the stitching algorithm is only tested on a frame-by-frame basis which does not realistically mimic the user perception of the output system quality as a complete video. We propose the use of a modified blending technique based on optimal seam selection and experimentally validate its superiority using precision, recall and F1measures on a frame-by-frame basis, while maintaining low computational complexity. Furthermore, we validate that the performance gains measured on a frame-by-frame basis are also evident when the stitched video output is evaluated as a single unit.

Mobile phones are becoming more and more ubiquitous with a large number of these devices having image/video capturing capabilities, connection capabilities and built-in rich sensory. This has encouraged the common user to capture more image/video content than ever before. However, this has created two interrelated problems: 1) while capturing some scene the user may want to get more information about it to make a decision (e.g. a buying one) without painful textual input on these mobile devices and while accounting for multiple meanings associated with a single image, as an image is worth a thousand words and 2) videos captured cannot be easily searched afterwards and hence forgotten due to the lack of proper indexing techniques. In this paper, we are presenting a system addressing the above two problems through a single solution by providing users with real-time automatically generated tags of their currently captured videos. The user can select/deselect from the automatic tags, thus tags can serve as visual query suggestions helping bridging the user's query intent. This same set of tags will be stored with the video for enabling easy content access afterwards.

Constructing a panoramic video out of multiple incoming live mobile video streams is a challenging problem. This problem involves multiple users live streaming the same scene from different angles, using their mobile phones, with the objective of constructing a panoramic video of the scene. The main challenge in this problem is the lack of coordination between the streaming users, resulting in too much, too little, or no overlap between incoming streams. To add to the challenge, the streaming users are generally free to move, which means that the amounts of overlap between the different streams are dynamically changing. In this paper, we propose a method for automatically coordinating between the streaming users, such that the quality of the resulting panoramic video is enhanced. The method works by analyzing the incoming video streams, and automatically providing active feedback to the streaming users. We investigate different methods for generating the active feedback and presenting it to the streaming users resulting in an improved panoramic video output compared to the case where no feedback is utilized.

User generated videos with mobile phone cameras are becoming more and more ubiquitous allowing people to share live content with remote parties, possibly in real-time. However with limited mobile phone capabilities, these videos are usually of small resolution, resulting in a small field of view for acceptable quality. Fortunately with the proliferation of video capture-enabled mobile phones, there is high chance that one or more persons will be shooting the same scene from different views. In this demonstration, we are showing an end-to-end system which receives video streams coming from different mobile phones, time synchronizes the streams and produces a single composite mosaic video, and all of this is done in real-time. The proposed system operates without coordination between users. The system has been tested under various capturing conditions such as indoor, outdoor, day and night conditions.

Lately, the usage of mobile live video streaming for video sharing has been growing steadily. However, because of the limitations presented by the capabilities of mobile phone video capturing devices, these video streams end up having either a low resolution or a small field of view. On the other hand, the ubiquity of video capture-capable mobile phones make the probability that more than one user will be recording the same scene from different views relatively high.