2,518 research outputs found
Review of Person Re-identification Techniques
Person re-identification across different surveillance cameras with disjoint
fields of view has become one of the most interesting and challenging subjects
in the area of intelligent video surveillance. Although several methods have
been developed and proposed, certain limitations and unresolved issues remain.
In all of the existing re-identification approaches, feature vectors are
extracted from segmented still images or video frames. Different similarity or
dissimilarity measures have been applied to these vectors. Some methods have
used simple constant metrics, whereas others have utilised models to obtain
optimised metrics. Some have created models based on local colour or texture
information, and others have built models based on the gait of people. In
general, the main objective of all these approaches is to achieve a
higher-accuracy rate and lowercomputational costs. This study summarises
several developments in recent literature and discusses the various available
methods used in person re-identification. Specifically, their advantages and
disadvantages are mentioned and compared.Comment: Published 201
Coplanar Repeats by Energy Minimization
This paper proposes an automated method to detect, group and rectify
arbitrarily-arranged coplanar repeated elements via energy minimization. The
proposed energy functional combines several features that model how planes with
coplanar repeats are projected into images and captures global interactions
between different coplanar repeat groups and scene planes. An inference
framework based on a recent variant of -expansion is described and fast
convergence is demonstrated. We compare the proposed method to two widely-used
geometric multi-model fitting methods using a new dataset of annotated images
containing multiple scene planes with coplanar repeats in varied arrangements.
The evaluation shows a significant improvement in the accuracy of
rectifications computed from coplanar repeats detected with the proposed method
versus those detected with the baseline methods.Comment: 14 pages with supplemental materials attache
Young Planetary Nebulae: Hubble Space Telescope Imaging and a New Morphological Classification System
Using Hubble Space Telescope images of 119 young planetary nebulae, most of
which have not previously been published, we have devised a comprehensive
morphological classification system for these objects. This system generalizes
a recently devised system for pre-planetary nebulae, which are the immediate
progenitors of planetary nebulae (PNs). Unlike previous classification studies,
we have focussed primarily on young PNs rather than all PNs, because the former
best show the influences or symmetries imposed on them by the dominant physical
processes operating at the first and primary stage of the shaping process.
Older PNs develop instabilities, interact with the ambient interstellar medium,
and are subject to the passage of photoionization fronts, all of which obscure
the underlying symmetries and geometries imposed early on. Our classification
system is designed to suffer minimal prejudice regarding the underlying
physical causes of the different shapes and structures seen in our PN sample,
however, in many cases, physical causes are readily suggested by the geometry,
along with the kinematics that have been measured in some systems. Secondary
characteristics in our system such as ansae indicate the impact of a jet upon a
slower-moving, prior wind; a waist is the signature of a strong equatorial
concentration of matter, whether it be outflowing or in a bound Keplerian disk,
and point symmetry indicates a secular trend, presumably precession, in the
orientation of the central driver of a rapid, collimated outflow.Comment: (to appear in The Astronomical Journal, March 2011.) The quality of
the figures as it appears in the arXiv pdf output is not up-to-par; the full
ms with high-quality figures is available by anonymous FTP at
ftp://ftp.astro.ucla.edu/pub/morris/sahai_AJ_360163.pd
Key-Pose Prediction in Cyclic Human Motion
In this paper we study the problem of estimating innercyclic time intervals
within repetitive motion sequences of top-class swimmers in a swimming channel.
Interval limits are given by temporal occurrences of key-poses, i.e.
distinctive postures of the body. A key-pose is defined by means of only one or
two specific features of the complete posture. It is often difficult to detect
such subtle features directly. We therefore propose the following method: Given
that we observe the swimmer from the side, we build a pictorial structure of
poselets to robustly identify random support poses within the regular motion of
a swimmer. We formulate a maximum likelihood model which predicts a key-pose
given the occurrences of multiple support poses within one stroke. The maximum
likelihood can be extended with prior knowledge about the temporal location of
a key-pose in order to improve the prediction recall. We experimentally show
that our models reliably and robustly detect key-poses with a high precision
and that their performance can be improved by extending the framework with
additional camera views.Comment: Accepted at WACV 2015, 8 pages, 3 figure
Recognizing the presence of hidden visual markers in digital images
As the promise of Virtual and Augmented Reality (VR and AR) becomes more realistic, an interesting aspect of our enhanced living environment includes the availability â indeed the potential ubiquity â of scannable markers. Such markers could represent an initial step into the AR and VR worlds. In this paper, we address the important question of how to recognise the presence of visual markers in freeform digital photos. We use a particularly challenging marker format that is only minimally constrained in structure, called Artcodes. Artcodes are a type of topological marker system enabling people, by following very simple drawing rules, to design markers that are both aesthetically beautiful and machine readable. Artcodes can be used to decorate the surface of any objects, and yet can also contain a hidden digital meaning. Like some other more commonly used markers (such as Barcodes, QR codes), it is possible to use codes to link physical objects to digital data, augmenting everyday objects. Obviously, in order to trigger the behaviour of scanning and further decoding of such codes, it is first necessary for devices to be aware of the presence of Artcodes in the image. Although considerable literature exists related to the detection of rigidly formatted structures and geometrical feature descriptors such as Harris, SIFT, and SURF, these approaches are not sufficient for describing freeform topological structures, such as Artcode images. In this paper, we propose a new topological feature descriptor that can be used in the detection of freeform topological markers, including Artcodes. This feature descriptor is called a Shape of Orientation Histogram (SOH). We construct this SOH feature vector by quantifying the level of symmetry and smoothness of the orientation histogram, and then use a Random Forest machine learning approach to classify images that contain Artcodes using the new feature vector. This system represents a potential first step for an eventual mobile device application that would detect where in an image such an unconstrained code appears. We also explain how the system handles imbalanced datasets â important for rare, handcrafted codes such as Artcodes â and how it is evaluated. Our experimental evaluation shows good performance of the proposed classification model in the detection of Artcodes: obtaining an overall accuracy of approx. 0.83, F2 measure 0.83, MCC 0.68, AUC-ROC 0.93, and AUC-PR 0.91
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