A variety of applications are emerging to support streaming video from mobile devices. However, many tasks can benefit from streaming specific content rather than the full video feed which may include irrelevant, private, or distracting content. We describe a system that allows users to capture and stream targeted video content captured with a mobile device. The application incorporates a variety of automatic and interactive techniques to identify and segment desired content, allowing the user to publish a more focused video stream.
We propose the ShareCam Problem: controlling a single robotic pan, tilt, zoom camera based on simultaneous frame requests from n online users. To solve it, we propose a new piecewise linear metric, Intersection Over Maximum (IOM), for the degree of satisfaction for each users. To maximize overall satisfaction, we present several algorithms. For a discrete set of m distinct zoom levels, we give an exact algorithm that runs in O(n 2 m) time. The algorithm can be distributed to run in O(nm) time at each client and in O(nlog n + mn) time at the server. The digital interactive video exploration and reflection (Diver) system lets users create virtual pathways through existing video content using a virtual camera and an annotation window for commentary. Users can post their Dives to the WebDiver server system to generate active collaboration, further repurposing, and discussion. Although our current work focuses on video records in learning research and educational practices, Diver can aid collaborative analysis of a broad array of visual data records, including simulations, 2D and 3D animations, and static works of art, photography, and text. In addition to the social and behavioral sciences, substantive application areas include medical visualization, astronomic data or cosmological models, military satellite intelligence, and ethnology and animal behavior. Diver-style user-centered video repurposing might also prove compelling for popular media with commercial application involving sports events, movies, television shows, and video gaming. Future technical development includes possible enhancements to the interface to support simultaneous display of multiple Dives on the same source content, a more fluid two-way relation between desktop Diver and WebDiver, and solutions to the current limitations on displaying and authoring time space cropped videos in a browser context. These developments support the tool's fundamentally collaborative, communication-oriented nature. FlySPEC is a video camera system designed for real-time remote operation. A hybrid design combines the high resolution of an optomechanical video camera with the wide field of view always available from a panoramic camera. The control system integrates requests from multiple users so that each controls a virtual camera. The control system seamlessly integrates manual and fully automatic control. It supports a range of options from untended automatic to full manual control. The system can also learn control strategies from user requests. Additionally, the panoramic view is always available for an intuitive interface, and objects are never out of view regardless of the zoom factor. We present the system architecture, an information-theoretic approach to combining panoramic and zoomed images to optimally satisfy user requests, and experimental results that show the FlySPEC system significantly assists users in a remote inspection tasks. This paper presents a system for protecting the privacy of specific individuals in video recordings. We address the following two problems: automatic people identification with limited labeled data, and human body obscuring with preserved structure and motion information. In order to address the first problem, we propose a new discriminative learning algorithm to improve people identification accuracy using limited training data labeled from the original video and imperfect pairwise constraints labeled from face obscured video data. We employ a robust face detection and tracking algorithm to obscure human faces in the video. Our experiments in a nursing home environment show that the system can obtain a high accuracy of people identification using limited labeled data and noisy pairwise constraints. The study result indicates that human subjects can perform reasonably well in labeling pairwise constraints with the face masked data. For the second problem, we propose a novel method of body obscuring, which removes the appearance information of the people while preserving rich structure and motion information. The proposed approach provides a way to minimize the risk of exposing the identities of the protected people while maximizing the use of the captured data for activity behavior analysis. 