23,617 research outputs found
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
ModDrop: adaptive multi-modal gesture recognition
We present a method for gesture detection and localisation based on
multi-scale and multi-modal deep learning. Each visual modality captures
spatial information at a particular spatial scale (such as motion of the upper
body or a hand), and the whole system operates at three temporal scales. Key to
our technique is a training strategy which exploits: i) careful initialization
of individual modalities; and ii) gradual fusion involving random dropping of
separate channels (dubbed ModDrop) for learning cross-modality correlations
while preserving uniqueness of each modality-specific representation. We
present experiments on the ChaLearn 2014 Looking at People Challenge gesture
recognition track, in which we placed first out of 17 teams. Fusing multiple
modalities at several spatial and temporal scales leads to a significant
increase in recognition rates, allowing the model to compensate for errors of
the individual classifiers as well as noise in the separate channels.
Futhermore, the proposed ModDrop training technique ensures robustness of the
classifier to missing signals in one or several channels to produce meaningful
predictions from any number of available modalities. In addition, we
demonstrate the applicability of the proposed fusion scheme to modalities of
arbitrary nature by experiments on the same dataset augmented with audio.Comment: 14 pages, 7 figure
SALSA: A Novel Dataset for Multimodal Group Behavior Analysis
Studying free-standing conversational groups (FCGs) in unstructured social
settings (e.g., cocktail party ) is gratifying due to the wealth of information
available at the group (mining social networks) and individual (recognizing
native behavioral and personality traits) levels. However, analyzing social
scenes involving FCGs is also highly challenging due to the difficulty in
extracting behavioral cues such as target locations, their speaking activity
and head/body pose due to crowdedness and presence of extreme occlusions. To
this end, we propose SALSA, a novel dataset facilitating multimodal and
Synergetic sociAL Scene Analysis, and make two main contributions to research
on automated social interaction analysis: (1) SALSA records social interactions
among 18 participants in a natural, indoor environment for over 60 minutes,
under the poster presentation and cocktail party contexts presenting
difficulties in the form of low-resolution images, lighting variations,
numerous occlusions, reverberations and interfering sound sources; (2) To
alleviate these problems we facilitate multimodal analysis by recording the
social interplay using four static surveillance cameras and sociometric badges
worn by each participant, comprising the microphone, accelerometer, bluetooth
and infrared sensors. In addition to raw data, we also provide annotations
concerning individuals' personality as well as their position, head, body
orientation and F-formation information over the entire event duration. Through
extensive experiments with state-of-the-art approaches, we show (a) the
limitations of current methods and (b) how the recorded multiple cues
synergetically aid automatic analysis of social interactions. SALSA is
available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure
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