1,172 research outputs found
Leveraging Contextual Cues for Generating Basketball Highlights
The massive growth of sports videos has resulted in a need for automatic
generation of sports highlights that are comparable in quality to the
hand-edited highlights produced by broadcasters such as ESPN. Unlike previous
works that mostly use audio-visual cues derived from the video, we propose an
approach that additionally leverages contextual cues derived from the
environment that the game is being played in. The contextual cues provide
information about the excitement levels in the game, which can be ranked and
selected to automatically produce high-quality basketball highlights. We
introduce a new dataset of 25 NCAA games along with their play-by-play stats
and the ground-truth excitement data for each basket. We explore the
informativeness of five different cues derived from the video and from the
environment through user studies. Our experiments show that for our study
participants, the highlights produced by our system are comparable to the ones
produced by ESPN for the same games.Comment: Proceedings of ACM Multimedia 201
Semantic analysis of field sports video using a petri-net of audio-visual concepts
The most common approach to automatic summarisation and highlight detection in sports video is to train an automatic classifier to detect semantic highlights based on occurrences of low-level features such as action replays, excited commentators or changes in a scoreboard. We propose an alternative approach based on the detection of perception concepts (PCs) and the construction of Petri-Nets which can be used for both semantic description and event detection within sports videos. Low-level algorithms for the detection of perception concepts using visual, aural and motion characteristics are proposed, and a series of Petri-Nets composed of perception concepts is formally defined to describe video content. We call this a Perception Concept Network-Petri Net (PCN-PN) model. Using PCN-PNs, personalized high-level semantic descriptions of video highlights can be facilitated and queries on high-level semantics can be achieved. A particular strength of this framework is that we can easily build semantic detectors based on PCN-PNs to search within sports videos and locate interesting events. Experimental results based on recorded sports
video data across three types of sports games (soccer, basketball and rugby), and each from multiple broadcasters, are used to illustrate the potential of this framework
Event detection in field sports video using audio-visual features and a support vector machine
In this paper, we propose a novel audio-visual feature-based framework for event detection in broadcast video of multiple different field sports. Features indicating significant events are selected and robust detectors built. These features are rooted in characteristics common to all genres of field sports. The evidence gathered by the feature detectors is combined by means of a support vector machine, which infers the occurrence of an event based on a model generated during a training phase. The system is tested generically across multiple genres of field sports including soccer, rugby, hockey, and Gaelic football and the results suggest that high event retrieval and content rejection statistics are achievable
Indirect Match Highlights Detection with Deep Convolutional Neural Networks
Highlights in a sport video are usually referred as actions that stimulate
excitement or attract attention of the audience. A big effort is spent in
designing techniques which find automatically highlights, in order to
automatize the otherwise manual editing process. Most of the state-of-the-art
approaches try to solve the problem by training a classifier using the
information extracted on the tv-like framing of players playing on the game
pitch, learning to detect game actions which are labeled by human observers
according to their perception of highlight. Obviously, this is a long and
expensive work. In this paper, we reverse the paradigm: instead of looking at
the gameplay, inferring what could be exciting for the audience, we directly
analyze the audience behavior, which we assume is triggered by events happening
during the game. We apply deep 3D Convolutional Neural Network (3D-CNN) to
extract visual features from cropped video recordings of the supporters that
are attending the event. Outputs of the crops belonging to the same frame are
then accumulated to produce a value indicating the Highlight Likelihood (HL)
which is then used to discriminate between positive (i.e. when a highlight
occurs) and negative samples (i.e. standard play or time-outs). Experimental
results on a public dataset of ice-hockey matches demonstrate the effectiveness
of our method and promote further research in this new exciting direction.Comment: "Social Signal Processing and Beyond" workshop, in conjunction with
ICIAP 201
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