7,612 research outputs found
Feature discovery and visualization of robot mission data using convolutional autoencoders and Bayesian nonparametric topic models
The gap between our ability to collect interesting data and our ability to
analyze these data is growing at an unprecedented rate. Recent algorithmic
attempts to fill this gap have employed unsupervised tools to discover
structure in data. Some of the most successful approaches have used
probabilistic models to uncover latent thematic structure in discrete data.
Despite the success of these models on textual data, they have not generalized
as well to image data, in part because of the spatial and temporal structure
that may exist in an image stream.
We introduce a novel unsupervised machine learning framework that
incorporates the ability of convolutional autoencoders to discover features
from images that directly encode spatial information, within a Bayesian
nonparametric topic model that discovers meaningful latent patterns within
discrete data. By using this hybrid framework, we overcome the fundamental
dependency of traditional topic models on rigidly hand-coded data
representations, while simultaneously encoding spatial dependency in our topics
without adding model complexity. We apply this model to the motivating
application of high-level scene understanding and mission summarization for
exploratory marine robots. Our experiments on a seafloor dataset collected by a
marine robot show that the proposed hybrid framework outperforms current
state-of-the-art approaches on the task of unsupervised seafloor terrain
characterization.Comment: 8 page
Unsupervised Action Proposal Ranking through Proposal Recombination
Recently, action proposal methods have played an important role in action
recognition tasks, as they reduce the search space dramatically. Most
unsupervised action proposal methods tend to generate hundreds of action
proposals which include many noisy, inconsistent, and unranked action
proposals, while supervised action proposal methods take advantage of
predefined object detectors (e.g., human detector) to refine and score the
action proposals, but they require thousands of manual annotations to train.
Given the action proposals in a video, the goal of the proposed work is to
generate a few better action proposals that are ranked properly. In our
approach, we first divide action proposal into sub-proposal and then use
Dynamic Programming based graph optimization scheme to select the optimal
combinations of sub-proposals from different proposals and assign each new
proposal a score. We propose a new unsupervised image-based actioness detector
that leverages web images and employs it as one of the node scores in our graph
formulation. Moreover, we capture motion information by estimating the number
of motion contours within each action proposal patch. The proposed method is an
unsupervised method that neither needs bounding box annotations nor video level
labels, which is desirable with the current explosion of large-scale action
datasets. Our approach is generic and does not depend on a specific action
proposal method. We evaluate our approach on several publicly available trimmed
and un-trimmed datasets and obtain better performance compared to several
proposal ranking methods. In addition, we demonstrate that properly ranked
proposals produce significantly better action detection as compared to
state-of-the-art proposal based methods
Context Trees: Augmenting Geospatial Trajectories with Context
Exposing latent knowledge in geospatial trajectories has the potential to
provide a better understanding of the movements of individuals and groups.
Motivated by such a desire, this work presents the context tree, a new
hierarchical data structure that summarises the context behind user actions in
a single model. We propose a method for context tree construction that augments
geospatial trajectories with land usage data to identify such contexts. Through
evaluation of the construction method and analysis of the properties of
generated context trees, we demonstrate the foundation for understanding and
modelling behaviour afforded. Summarising user contexts into a single data
structure gives easy access to information that would otherwise remain latent,
providing the basis for better understanding and predicting the actions and
behaviours of individuals and groups. Finally, we also present a method for
pruning context trees, for use in applications where it is desirable to reduce
the size of the tree while retaining useful information
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
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