1,188 research outputs found
Joint Target Tracking and Recognition Using Shape-based Generative Model
Recently a generative model that combines both of identity and view manifolds was proposed for multi-view shape modeling that was originally used for pose estimation and recognition of civilian vehicles from image sequences. In this thesis, we extend this model to both civilian and military vehicles, and examine its effectiveness for real-world automated target tracking and recognition (ATR) applications in both infrared and visible image sequences. A particle filter-based ATR algorithm is introduced where the generative model is used for shape interpolation along both the view and identity manifolds. The ATR algorithm is tested on the newly released SENSIAC (Military Sensing Information Analysis Center) infrared database along with some visible-band image sequences. Overall tracking and recognition performance is evaluated in terms of the accuracy of 3D position/pose estimation and target classification.</School of Electrical & Computer Engineerin
Advanced machine learning approaches for target detection, tracking and recognition
This dissertation addresses the key technical components of an Automatic Target Recognition (ATR) system namely: target detection, tracking, learning and recognition. Novel solutions are proposed for each component of the ATR system based on several new advances in the field of computer vision and machine learning. Firstly, we introduce a simple and elegant feature, RelCom, and a boosted feature selection method to achieve a very low computational complexity target detector. Secondly, we present a particle filter based target tracking algorithm that uses a quad histogram based appearance model along with online feature selection. Further, we improve the tracking performance by means of online appearance learning where appearance learning is cast as an Adaptive Kalman filtering (AKF) problem which we formulate using both covariance matching and, for the first time in a visual tracking application, the recent autocovariance least-squares (ALS) method. Then, we introduce an integrated tracking and recognition system that uses two generative models to accommodate the pose variations and maneuverability of different ground targets. Specifically, a tensor-based generative model is used for multi-view target representation that can synthesize unseen poses, and can be trained from a small set of signatures. In addition, a target-dependent kinematic model is invoked to characterize the target dynamics. Both generative models are integrated in a graphical framework for joint estimation of the target's kinematics, pose, and discrete valued identity. Finally, for target recognition we advocate the concept of a continuous identity manifold that captures both inter-class and intra-class shape variability among training targets. A hemispherical view manifold is used for modeling the view-dependent appearance. In addition to being able to deal with arbitrary view variations, this model can determine the target identity at both class and sub-class levels, for targets not present in the training data. The proposed components of the ATR system enable us to perform low computational complexity target detection with low false alarm rates, robust tracking of targets under challenging circumstances and recognition of target identities at both class and sub-class levels. Experiments on real and simulated data confirm the performance of the proposed components with promising results
Estimation of vector fields in unconstrained and inequality constrained variational problems for segmentation and registration
Vector fields arise in many problems of computer vision, particularly in non-rigid registration. In this paper, we develop coupled partial differential equations (PDEs) to estimate vector fields that define the deformation between
objects, and the contour or surface that defines the segmentation of the objects as well.We also explore the utility of inequality constraints applied to variational problems in vision such as estimation of deformation fields in non-rigid registration and tracking. To solve inequality constrained vector
field estimation problems, we apply tools from the Kuhn-Tucker theorem in optimization theory. Our technique differs from recently popular joint segmentation and registration algorithms, particularly in its coupled set of PDEs derived from the same set of energy terms for registration and
segmentation. We present both the theory and results that demonstrate our approach
F-formation Detection: Individuating Free-standing Conversational Groups in Images
Detection of groups of interacting people is a very interesting and useful
task in many modern technologies, with application fields spanning from
video-surveillance to social robotics. In this paper we first furnish a
rigorous definition of group considering the background of the social sciences:
this allows us to specify many kinds of group, so far neglected in the Computer
Vision literature. On top of this taxonomy, we present a detailed state of the
art on the group detection algorithms. Then, as a main contribution, we present
a brand new method for the automatic detection of groups in still images, which
is based on a graph-cuts framework for clustering individuals; in particular we
are able to codify in a computational sense the sociological definition of
F-formation, that is very useful to encode a group having only proxemic
information: position and orientation of people. We call the proposed method
Graph-Cuts for F-formation (GCFF). We show how GCFF definitely outperforms all
the state of the art methods in terms of different accuracy measures (some of
them are brand new), demonstrating also a strong robustness to noise and
versatility in recognizing groups of various cardinality.Comment: 32 pages, submitted to PLOS On
SEGMENTATION, RECOGNITION, AND ALIGNMENT OF COLLABORATIVE GROUP MOTION
Modeling and recognition of human motion in videos has broad applications in behavioral biometrics, content-based visual data analysis, security and surveillance, as well as designing interactive environments. Significant progress has been made in the past two decades by way of new models, methods, and implementations. In this dissertation, we focus our attention on a relatively less investigated sub-area called collaborative group motion analysis. Collaborative group motions are those that typically involve multiple objects, wherein the motion patterns of individual objects may vary significantly in both space and time, but the collective motion pattern of the ensemble allows characterization in terms of geometry and statistics. Therefore, the motions or activities of an individual object constitute local information. A framework to synthesize all local information into a holistic view, and to explicitly characterize interactions among objects, involves large scale global reasoning, and is of significant complexity. In this dissertation, we first review relevant previous contributions on human motion/activity modeling and recognition, and then propose several approaches to answer a sequence of traditional vision questions including 1) which of the motion elements among all are the ones relevant to a group motion pattern of interest (Segmentation); 2) what is the underlying motion pattern (Recognition); and 3) how two motion ensembles are similar and how we can 'optimally' transform one to match the other (Alignment). Our primary practical scenario is American football play, where the corresponding problems are 1) who are offensive players; 2) what are the offensive strategy they are using; and 3) whether two plays are using the same strategy and how we can remove the spatio-temporal misalignment between them due to internal or external factors. The proposed approaches discard traditional modeling paradigm but explore either concise descriptors, hierarchies, stochastic mechanism, or compact generative model to achieve both effectiveness and efficiency.
In particular, the intrinsic geometry of the spaces of the involved features/descriptors/quantities is exploited and statistical tools are established on these nonlinear manifolds. These initial attempts have identified new challenging problems in complex motion analysis, as well as in more general tasks in video dynamics. The insights gained from nonlinear geometric modeling and analysis in this dissertation may hopefully be useful toward a broader class of computer vision applications
Programming by Demonstration on Riemannian Manifolds
This thesis presents a Riemannian approach to Programming by Demonstration (PbD).
It generalizes an existing PbD method from Euclidean manifolds to Riemannian manifolds.
In this abstract, we review the objectives, methods and contributions of the presented
approach.
OBJECTIVES
PbD aims at providing a user-friendly method for skill transfer between human and
robot. It enables a user to teach a robot new tasks using few demonstrations. In order
to surpass simple record-and-replay, methods for PbD need to \u2018understand\u2019 what to
imitate; they need to extract the functional goals of a task from the demonstration data.
This is typically achieved through the application of statisticalmethods.
The variety of data encountered in robotics is large. Typical manipulation tasks involve
position, orientation, stiffness, force and torque data. These data are not solely
Euclidean. Instead, they originate from a variety of manifolds, curved spaces that are
only locally Euclidean. Elementary operations, such as summation, are not defined on
manifolds. Consequently, standard statistical methods are not well suited to analyze
demonstration data that originate fromnon-Euclidean manifolds. In order to effectively
extract what-to-imitate, methods for PbD should take into account the underlying geometry
of the demonstration manifold; they should be geometry-aware.
Successful task execution does not solely depend on the control of individual task
variables. By controlling variables individually, a task might fail when one is perturbed
and the others do not respond. Task execution also relies on couplings among task variables.
These couplings describe functional relations which are often called synergies. In
order to understand what-to-imitate, PbDmethods should be able to extract and encode
synergies; they should be synergetic.
In unstructured environments, it is unlikely that tasks are found in the same scenario
twice. The circumstances under which a task is executed\u2014the task context\u2014are more
likely to differ each time it is executed. Task context does not only vary during task execution,
it also varies while learning and recognizing tasks. To be effective, a robot should
be able to learn, recognize and synthesize skills in a variety of familiar and unfamiliar
contexts; this can be achieved when its skill representation is context-adaptive.
THE RIEMANNIAN APPROACH
In this thesis, we present a skill representation that is geometry-aware, synergetic and
context-adaptive. The presented method is probabilistic; it assumes that demonstrations
are samples from an unknown probability distribution. This distribution is approximated
using a Riemannian GaussianMixtureModel (GMM).
Instead of using the \u2018standard\u2019 Euclidean Gaussian, we rely on the Riemannian Gaussian\u2014
a distribution akin the Gaussian, but defined on a Riemannian manifold. A Riev
mannian manifold is a manifold\u2014a curved space which is locally Euclidean\u2014that provides
a notion of distance. This notion is essential for statistical methods as such methods
rely on a distance measure. Examples of Riemannian manifolds in robotics are: the
Euclidean spacewhich is used for spatial data, forces or torques; the spherical manifolds,
which can be used for orientation data defined as unit quaternions; and Symmetric Positive
Definite (SPD) manifolds, which can be used to represent stiffness and manipulability.
The Riemannian Gaussian is intrinsically geometry-aware. Its definition is based on
the geometry of the manifold, and therefore takes into account the manifold curvature.
In robotics, the manifold structure is often known beforehand. In the case of PbD, it follows
from the structure of the demonstration data. Like the Gaussian distribution, the
Riemannian Gaussian is defined by a mean and covariance. The covariance describes
the variance and correlation among the state variables. These can be interpreted as local
functional couplings among state variables: synergies. This makes the Riemannian
Gaussian synergetic. Furthermore, information encoded in multiple Riemannian Gaussians
can be fused using the Riemannian product of Gaussians. This feature allows us to
construct a probabilistic context-adaptive task representation.
CONTRIBUTIONS
In particular, this thesis presents a generalization of existing methods of PbD, namely
GMM-GMR and TP-GMM. This generalization involves the definition ofMaximum Likelihood
Estimate (MLE), Gaussian conditioning and Gaussian product for the Riemannian
Gaussian, and the definition of ExpectationMaximization (EM) and GaussianMixture
Regression (GMR) for the Riemannian GMM. In this generalization, we contributed
by proposing to use parallel transport for Gaussian conditioning. Furthermore, we presented
a unified approach to solve the aforementioned operations using aGauss-Newton
algorithm. We demonstrated how synergies, encoded in a Riemannian Gaussian, can be
transformed into synergetic control policies using standard methods for LinearQuadratic
Regulator (LQR). This is achieved by formulating the LQR problem in a (Euclidean) tangent
space of the Riemannian manifold. Finally, we demonstrated how the contextadaptive
Task-Parameterized Gaussian Mixture Model (TP-GMM) can be used for context
inference\u2014the ability to extract context from demonstration data of known tasks.
Our approach is the first attempt of context inference in the light of TP-GMM. Although
effective, we showed that it requires further improvements in terms of speed and reliability.
The efficacy of the Riemannian approach is demonstrated in a variety of scenarios.
In shared control, the Riemannian Gaussian is used to represent control intentions of a
human operator and an assistive system. Doing so, the properties of the Gaussian can
be employed to mix their control intentions. This yields shared-control systems that
continuously re-evaluate and assign control authority based on input confidence. The
context-adaptive TP-GMMis demonstrated in a Pick & Place task with changing pick and
place locations, a box-taping task with changing box sizes, and a trajectory tracking task
typically found in industr
State of the Art in Dense Monocular Non-Rigid 3D Reconstruction
3D reconstruction of deformable (or non-rigid) scenes from a set of monocular2D image observations is a long-standing and actively researched area ofcomputer vision and graphics. It is an ill-posed inverse problem,since--without additional prior assumptions--it permits infinitely manysolutions leading to accurate projection to the input 2D images. Non-rigidreconstruction is a foundational building block for downstream applicationslike robotics, AR/VR, or visual content creation. The key advantage of usingmonocular cameras is their omnipresence and availability to the end users aswell as their ease of use compared to more sophisticated camera set-ups such asstereo or multi-view systems. This survey focuses on state-of-the-art methodsfor dense non-rigid 3D reconstruction of various deformable objects andcomposite scenes from monocular videos or sets of monocular views. It reviewsthe fundamentals of 3D reconstruction and deformation modeling from 2D imageobservations. We then start from general methods--that handle arbitrary scenesand make only a few prior assumptions--and proceed towards techniques makingstronger assumptions about the observed objects and types of deformations (e.g.human faces, bodies, hands, and animals). A significant part of this STAR isalso devoted to classification and a high-level comparison of the methods, aswell as an overview of the datasets for training and evaluation of thediscussed techniques. We conclude by discussing open challenges in the fieldand the social aspects associated with the usage of the reviewed methods.<br
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