9,131 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
Automatic Understanding of Image and Video Advertisements
There is more to images than their objective physical content: for example,
advertisements are created to persuade a viewer to take a certain action. We
propose the novel problem of automatic advertisement understanding. To enable
research on this problem, we create two datasets: an image dataset of 64,832
image ads, and a video dataset of 3,477 ads. Our data contains rich annotations
encompassing the topic and sentiment of the ads, questions and answers
describing what actions the viewer is prompted to take and the reasoning that
the ad presents to persuade the viewer ("What should I do according to this ad,
and why should I do it?"), and symbolic references ads make (e.g. a dove
symbolizes peace). We also analyze the most common persuasive strategies ads
use, and the capabilities that computer vision systems should have to
understand these strategies. We present baseline classification results for
several prediction tasks, including automatically answering questions about the
messages of the ads.Comment: To appear in CVPR 2017; data available on
http://cs.pitt.edu/~kovashka/ad
ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector
Given the ability to directly manipulate image pixels in the digital input
space, an adversary can easily generate imperceptible perturbations to fool a
Deep Neural Network (DNN) image classifier, as demonstrated in prior work. In
this work, we propose ShapeShifter, an attack that tackles the more challenging
problem of crafting physical adversarial perturbations to fool image-based
object detectors like Faster R-CNN. Attacking an object detector is more
difficult than attacking an image classifier, as it needs to mislead the
classification results in multiple bounding boxes with different scales.
Extending the digital attack to the physical world adds another layer of
difficulty, because it requires the perturbation to be robust enough to survive
real-world distortions due to different viewing distances and angles, lighting
conditions, and camera limitations. We show that the Expectation over
Transformation technique, which was originally proposed to enhance the
robustness of adversarial perturbations in image classification, can be
successfully adapted to the object detection setting. ShapeShifter can generate
adversarially perturbed stop signs that are consistently mis-detected by Faster
R-CNN as other objects, posing a potential threat to autonomous vehicles and
other safety-critical computer vision systems
Discovery and recognition of motion primitives in human activities
We present a novel framework for the automatic discovery and recognition of
motion primitives in videos of human activities. Given the 3D pose of a human
in a video, human motion primitives are discovered by optimizing the `motion
flux', a quantity which captures the motion variation of a group of skeletal
joints. A normalization of the primitives is proposed in order to make them
invariant with respect to a subject anatomical variations and data sampling
rate. The discovered primitives are unknown and unlabeled and are
unsupervisedly collected into classes via a hierarchical non-parametric Bayes
mixture model. Once classes are determined and labeled they are further
analyzed for establishing models for recognizing discovered primitives. Each
primitive model is defined by a set of learned parameters.
Given new video data and given the estimated pose of the subject appearing on
the video, the motion is segmented into primitives, which are recognized with a
probability given according to the parameters of the learned models.
Using our framework we build a publicly available dataset of human motion
primitives, using sequences taken from well-known motion capture datasets. We
expect that our framework, by providing an objective way for discovering and
categorizing human motion, will be a useful tool in numerous research fields
including video analysis, human inspired motion generation, learning by
demonstration, intuitive human-robot interaction, and human behavior analysis
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