Video browsing interfaces and applications: a review

We present a comprehensive review of the state of the art in video browsing and retrieval systems, with special emphasis on interfaces and applications. There has been a significant increase in activity (e.g., storage, retrieval, and sharing) employing video data in the past decade, both for personal and professional use. The ever-growing amount of video content available for human consumption and the inherent characteristics of video data—which, if presented in its raw format, is rather unwieldy and costly—have become driving forces for the development of more effective solutions to present video contents and allow rich user interaction. As a result, there are many contemporary research efforts toward developing better video browsing solutions, which we summarize. We review more than 40 different video browsing and retrieval interfaces and classify them into three groups: applications that use video-player-like interaction, video retrieval applications, and browsing solutions based on video surrogates. For each category, we present a summary of existing work, highlight the technical aspects of each solution, and compare them against each other

The Evolution of First Person Vision Methods: A Survey

The emergence of new wearable technologies such as action cameras and smart-glasses has increased the interest of computer vision scientists in the First Person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with First Person Vision recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real-time, is expected. Current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user machine interaction and so on. This paper summarizes the evolution of the state of the art in First Person Vision video analysis between 1997 and 2014, highlighting, among others, most commonly used features, methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart Glasses, Computer Vision, Video Analytics, Human-machine Interactio

The TREC-2002 video track report

TREC-2002 saw the second running of the Video Track, the goal of which was to promote progress in content-based retrieval from digital video via open, metrics-based evaluation. The track used 73.3 hours of publicly available digital video (in MPEG-1/VCD format) downloaded by the participants directly from the Internet Archive (Prelinger Archives) (internetarchive, 2002) and some from the Open Video Project (Marchionini, 2001). The material comprised advertising, educational, industrial, and amateur films produced between the 1930's and the 1970's by corporations, nonprofit organizations, trade associations, community and interest groups, educational institutions, and individuals. 17 teams representing 5 companies and 12 universities - 4 from Asia, 9 from Europe, and 4 from the US - participated in one or more of three tasks in the 2001 video track: shot boundary determination, feature extraction, and search (manual or interactive). Results were scored by NIST using manually created truth data for shot boundary determination and manual assessment of feature extraction and search results. This paper is an introduction to, and an overview of, the track framework - the tasks, data, and measures - the approaches taken by the participating groups, the results, and issues regrading the evaluation. For detailed information about the approaches and results, the reader should see the various site reports in the final workshop proceedings

Vision-Based Production of Personalized Video

In this paper we present a novel vision-based system for the automated production of personalised video souvenirs for visitors in leisure and cultural heritage venues. Visitors are visually identified and tracked through a camera network. The system produces a personalized DVD souvenir at the end of a visitor’s stay allowing visitors to relive their experiences. We analyze how we identify visitors by fusing facial and body features, how we track visitors, how the tracker recovers from failures due to occlusions, as well as how we annotate and compile the final product. Our experiments demonstrate the feasibility of the proposed approach

Multimodal Indexing of Presentation Videos

This thesis presents four novel methods to help users efficiently and effectively retrieve information from unstructured and unsourced multimedia sources, in particular the increasing amount and variety of presentation videos such as those in e-learning, conference recordings, corporate talks, and student presentations. We demonstrate a system to summarize, index and cross-reference such videos, and measure the quality of the produced indexes as perceived by the end users. We introduce four major semantic indexing cues: text, speaker faces, graphics, and mosaics, going beyond standard tag based searches and simple video playbacks. This work aims at recognizing visual content "in the wild", where the system cannot rely on any additional information besides the video itself. For text, within a scene text detection and recognition framework, we present a novel locally optimal adaptive binarization algorithm, implemented with integral histograms. It determines of an optimal threshold that maximizes the between-classes variance within a subwindow, with computational complexity independent from the size of the window itself. We obtain character recognition rates of 74%, as validated against ground truth of 8 presentation videos spanning over 1 hour and 45 minutes, which almost doubles the baseline performance of an open source OCR engine. For speaker faces, we detect, track, match, and finally select a humanly preferred face icon per speaker, based on three quality measures: resolution, amount of skin, and pose. We register a 87% accordance (51 out of 58 speakers) between the face indexes automatically generated from three unstructured presentation videos of approximately 45 minutes each, and human preferences recorded through Mechanical Turk experiments. For diagrams, we locate graphics inside frames showing a projected slide, cluster them according to an on-line algorithm based on a combination of visual and temporal information, and select and color-correct their representatives to match human preferences recorded through Mechanical Turk experiments. We register 71% accuracy (57 out of 81 unique diagrams properly identified, selected and color-corrected) on three hours of videos containing five different presentations. For mosaics, we combine two existing suturing measures, to extend video images into in-the-world coordinate system. A set of frames to be registered into a mosaic are sampled according to the PTZ camera movement, which is computed through least square estimation starting from the luminance constancy assumption. A local features based stitching algorithm is then applied to estimate the homography among a set of video frames and median blending is used to render pixels in overlapping regions of the mosaic. For two of these indexes, namely faces and diagrams, we present two novel MTurk-derived user data collections to determine viewer preferences, and show that they are matched in selection by our methods. The net result work of this thesis allows users to search, inside a video collection as well as within a single video clip, for a segment of presentation by professor X on topic Y, containing graph Z