1,872 research outputs found
SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos
In this paper, we introduce SoccerNet, a benchmark for action spotting in
soccer videos. The dataset is composed of 500 complete soccer games from six
main European leagues, covering three seasons from 2014 to 2017 and a total
duration of 764 hours. A total of 6,637 temporal annotations are automatically
parsed from online match reports at a one minute resolution for three main
classes of events (Goal, Yellow/Red Card, and Substitution). As such, the
dataset is easily scalable. These annotations are manually refined to a one
second resolution by anchoring them at a single timestamp following
well-defined soccer rules. With an average of one event every 6.9 minutes, this
dataset focuses on the problem of localizing very sparse events within long
videos. We define the task of spotting as finding the anchors of soccer events
in a video. Making use of recent developments in the realm of generic action
recognition and detection in video, we provide strong baselines for detecting
soccer events. We show that our best model for classifying temporal segments of
length one minute reaches a mean Average Precision (mAP) of 67.8%. For the
spotting task, our baseline reaches an Average-mAP of 49.7% for tolerances
ranging from 5 to 60 seconds. Our dataset and models are available at
https://silviogiancola.github.io/SoccerNet.Comment: CVPR Workshop on Computer Vision in Sports 201
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
Enhancing Energy Minimization Framework for Scene Text Recognition with Top-Down Cues
Recognizing scene text is a challenging problem, even more so than the
recognition of scanned documents. This problem has gained significant attention
from the computer vision community in recent years, and several methods based
on energy minimization frameworks and deep learning approaches have been
proposed. In this work, we focus on the energy minimization framework and
propose a model that exploits both bottom-up and top-down cues for recognizing
cropped words extracted from street images. The bottom-up cues are derived from
individual character detections from an image. We build a conditional random
field model on these detections to jointly model the strength of the detections
and the interactions between them. These interactions are top-down cues
obtained from a lexicon-based prior, i.e., language statistics. The optimal
word represented by the text image is obtained by minimizing the energy
function corresponding to the random field model. We evaluate our proposed
algorithm extensively on a number of cropped scene text benchmark datasets,
namely Street View Text, ICDAR 2003, 2011 and 2013 datasets, and IIIT 5K-word,
and show better performance than comparable methods. We perform a rigorous
analysis of all the steps in our approach and analyze the results. We also show
that state-of-the-art convolutional neural network features can be integrated
in our framework to further improve the recognition performance
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