4,000 research outputs found
Unsupervised Discovery of Parts, Structure, and Dynamics
Humans easily recognize object parts and their hierarchical structure by
watching how they move; they can then predict how each part moves in the
future. In this paper, we propose a novel formulation that simultaneously
learns a hierarchical, disentangled object representation and a dynamics model
for object parts from unlabeled videos. Our Parts, Structure, and Dynamics
(PSD) model learns to, first, recognize the object parts via a layered image
representation; second, predict hierarchy via a structural descriptor that
composes low-level concepts into a hierarchical structure; and third, model the
system dynamics by predicting the future. Experiments on multiple real and
synthetic datasets demonstrate that our PSD model works well on all three
tasks: segmenting object parts, building their hierarchical structure, and
capturing their motion distributions.Comment: ICLR 2019. The first two authors contributed equally to this wor
Saying What You're Looking For: Linguistics Meets Video Search
We present an approach to searching large video corpora for video clips which
depict a natural-language query in the form of a sentence. This approach uses
compositional semantics to encode subtle meaning that is lost in other systems,
such as the difference between two sentences which have identical words but
entirely different meaning: "The person rode the horse} vs. \emph{The horse
rode the person". Given a video-sentence pair and a natural-language parser,
along with a grammar that describes the space of sentential queries, we produce
a score which indicates how well the video depicts the sentence. We produce
such a score for each video clip in a corpus and return a ranked list of clips.
Furthermore, this approach addresses two fundamental problems simultaneously:
detecting and tracking objects, and recognizing whether those tracks depict the
query. Because both tracking and object detection are unreliable, this uses
knowledge about the intended sentential query to focus the tracker on the
relevant participants and ensures that the resulting tracks are described by
the sentential query. While earlier work was limited to single-word queries
which correspond to either verbs or nouns, we show how one can search for
complex queries which contain multiple phrases, such as prepositional phrases,
and modifiers, such as adverbs. We demonstrate this approach by searching for
141 queries involving people and horses interacting with each other in 10
full-length Hollywood movies.Comment: 13 pages, 8 figure
Real-Time Salient Closed Boundary Tracking via Line Segments Perceptual Grouping
This paper presents a novel real-time method for tracking salient closed
boundaries from video image sequences. This method operates on a set of
straight line segments that are produced by line detection. The tracking scheme
is coherently integrated into a perceptual grouping framework in which the
visual tracking problem is tackled by identifying a subset of these line
segments and connecting them sequentially to form a closed boundary with the
largest saliency and a certain similarity to the previous one. Specifically, we
define a new tracking criterion which combines a grouping cost and an area
similarity constraint. The proposed criterion makes the resulting boundary
tracking more robust to local minima. To achieve real-time tracking
performance, we use Delaunay Triangulation to build a graph model with the
detected line segments and then reduce the tracking problem to finding the
optimal cycle in this graph. This is solved by our newly proposed closed
boundary candidates searching algorithm called "Bidirectional Shortest Path
(BDSP)". The efficiency and robustness of the proposed method are tested on
real video sequences as well as during a robot arm pouring experiment.Comment: 7 pages, 8 figures, The 2017 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2017) submission ID 103
Predicting Out-of-View Feature Points for Model-Based Camera Pose Estimation
In this work we present a novel framework that uses deep learning to predict
object feature points that are out-of-view in the input image. This system was
developed with the application of model-based tracking in mind, particularly in
the case of autonomous inspection robots, where only partial views of the
object are available. Out-of-view prediction is enabled by applying scaling to
the feature point labels during network training. This is combined with a
recurrent neural network architecture designed to provide the final prediction
layers with rich feature information from across the spatial extent of the
input image. To show the versatility of these out-of-view predictions, we
describe how to integrate them in both a particle filter tracker and an
optimisation based tracker. To evaluate our work we compared our framework with
one that predicts only points inside the image. We show that as the amount of
the object in view decreases, being able to predict outside the image bounds
adds robustness to the final pose estimation.Comment: Submitted to IROS 201
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