Abstract
In this paper, we present an extension to the real-time motion
template research for computer vision as previously developed
in (Davis 1997). The underlying representation is a Motion History
Image (MHI) that temporally layers consecutive image silhouettes
(or motion properties) of a moving person into a single template
form. Originally a global, label-based method was used for
recognition. In this work, we construct a more localized motion
characterization for the MHI that extracts motion orientations in
real-time. Basically, directional motion information can be recovered
directly from the intensity gradients within the MHI. In addition,
we
provide a few simple motion features using these orientations. The
approach presented is implemented in real-time on a standard PC
platform employing optimized routines, developed in part for this
research, from the Intel Computer Vision Library (CVLib). We conclude
with an overview of this library and also a performance evaluation
in terms of this research.
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Abstract
This paper describes a fast and robust approach for recovering structure
and motion from video frames. It first describes a robust recursive
factorization method for affine projection. Using the Least Median
of
Squares (LMedS) criterion, the method estimates the dominant 3D affine
motion and discards feature points regarded as outliers. The
computational cost of the overall procedure is reduced by combining
this
robust-statistics-based method with a recursive factorization method
that
can at each frame provide the updated 3D structure of an object at
a
fixed computational cost by using the principal component analysis.
This
paper then describes experiments with synthetic data and with real
image
sequences, the results of which demonstrate that the method can be
used
to estimate the dominant structure and the motion robustly and in
real-time on an off-the-shelf PC.
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Abstract
We present a robust, frame-rate pupil detector technique, based on
an active
illumination scheme, used for gaze estimation. The pupil detector uses
two
light sources synchronized with the even and odd fields of the video
signal
(interlaced frames), to create bright and dark pupil images. The
retro-reflectivity property of the eye is exploited by placing an infra-red
(IR) light source close to the camera's optical axis resulting in an
image
with a bright pupil. A similar off axis IR source generates an image
with dark
pupils. Pupils are detected from the thresholded difference of the
bright and
dark pupil images. After a calibration procedure, the vector computed
from the
pupil center to the center of the corneal glints generated from light
sources
is used to estimate the gaze position. The frame-rate gaze estimator
prototype
is currently being demonstrated in a docked 300 MHz IBM Thinkpad with
a PCI
frame grabber, using interlaced frames of resolution 640x480x8 bits.
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Abstract:
This paper presents a novel algorithm for detecting moving
objects from a static background scene that contains shading and shadows
using color images. We develop a robust and efficiently computed
background
subtraction algorithm that is able to cope with local illumination
changes,
such as shadows and highlights, as well as global illumination changes.
The
algorithm is based on a proposed computational color model which separates
the brightness from the chromaticity component. We have applied
this method
to real image sequences of both indoor and outdoor scenes. The
results,
which demonstrate the system's performance, and some speed up techniques
we
employed in our implementation are also shown.
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Abstract
Recent investigations have shown the advantages of keeping multiple
hypotheses during visual tracking. In this paper we explore an
alternative method that keeps just a single hypothesis per tracked
object for computational efficiency, but displays robust performance
and recovery from error by using segmentation provided by a stereo
module. The method is implemented in the domain of people-tracking,
using a novel combination of stereo information for continuous
detection and intensity image correlation for tracking. Real-time
stereo provides extended information for 3D detection and tracking,
even in the presence of crowded scenes, obscuring objects, and large
scale changes. We are able to reliably detect and track people
in
natural environments, on an implemented system that runs at more than
10 Hz on standard PC hardware.
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Abstract:
Background subtraction is a method typically used to segment moving
regions in image sequences by comparing each new frame to a model of
the
scene background. We present a non-parametric background model and
a
background subtraction approach. The model can handle situations where
the background of the scene is cluttered and not completely static
but
contains small motions such as tree branches and bushes. The model
estimates the probability of observing pixel intensity values based
on a
sample of intensity values for each pixel. The model adapts quickly
to
changes in the scene which enables very sensitive detection of moving
targets. We also show how the model can use color information to
suppress detection of shadows. The implementation of the model runs
in
real-time for both gray level and color imagery. Evaluation shows that
this approach achieves very sensitive detection with very low false
alarm rates.
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Abstract
Foreground/background segmentation is a technique that shares the same
goals of Blue-screen chroma keying - to separate the foreground from the
background - but does so without the strong requirement of the existence
of a known screen behind the subject of interest. Instead, a model of the
background is built using historic and weak prior knowledge. Because of
the computation-intensive nature of model-based segmentation algorithms,
foreground/background segmentation at video rates is a challenging problem
without the use of custom hardware or high-end workstations. We discuss
techniques used in the implementation of a real-time foreground/background
segmentation algorithm on a general-purpose consumer grade PC. In particular
we demonstrate optimization techniques in the implementation of three critical
sections of our algorithm: Binary Morphological Filter, Directional Morphological
Filter and Region Flood Fill. These techniques exploit the instruction
set of the Pentium®II and Pentium®III processors allowing video
segmentation of 320x240 color frames at 25 fps. The optimized critical
sections may be immediately used in a plethora of other applications. Moreover,
the optimization methodology provides useful insight into the optimization
of other image processing and computer vision techniques, such as edge
detection, object boundary localization, and morphological pre- and post-
processing.
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Abstract
Real-time video stabilization is computed from point-to-line
correspondences using linear-programming. The implementation of the
stabilizer requires special techniques for (i) frame grabbing, (ii)
computing point-to-line correspondences, (iii) linear-program solving
and (iv) image warping. Timing and real-time profiling are also
addressed.
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Abstract
In this paper, we describe our experiences in developing real-time
computer
vision applications for Intel Pentium III based Windows NT workstations.
Specifically, we discuss how to optimize your code, efficiently utilize
memory and the file system, utilize multiple CPUs, get video input,
and
benchmark your code. Intrinsic soft real-time features of Windows
NT are
discussed, as well as hard real-time extensions. An optimized real-time
optical flow application is given. Empirical results of memory subsystems
and cache scheduling issues are also reported.
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Abstract
We provide a fast algorithm to perform image-based tracking, which
relies on
the selective integration of a small subset of pixels that contain
a lot of
information about the state variables to be estimated. The resulting
dramatic decrease in the number of pixels to process results in a
substantial speedup of the basic tracking algorithm. We have used this
new
method within a surveillance application, where it will enable new
capabilities of the system, i.e. real-time, dynamic background subtraction
from a panning and tilting camera.
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Abstract
Optic flow has been a research topic of interest for many years. It
has, until recently, been largely inapplicable to real-time video
applications due to its computationally expensive nature. This paper
presents a new,
reliable flow technique called dynamic region matching, based
on
the work of Anandan, Lucas and Kanade, and Okutomi and Kanade, which
can
be combined with a motion detection algorithm (from stationary or
stabilised camera image streams) to allow flow-based analyses of moving
entities in
real-time. If flow vectors need only be calculated for ``moving'' pixels,
then the computation time is greatly reduced, making it applicable
to
real-time implementation on modest computational platforms (such as
standard Pentium
II based PCs). Applying this flow technique to moving entities provides
some straightforward primitives for analysing the motion of those objects.
Specifically, in this paper, methods are presented for: analysing rigidity
and cyclic motion using residual flow; and determining
self-occlusion and disambiguating multiple, mutually occluding entities
using pixel
contention.
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Abstract
Over the last three years there has been increased interest in
photo-realistic modelling and rendering techniques and a surge in popularity
of image-based rendering. These techniques aim to accurately model
reality
in order to generate virtual imagery that is indistinguishable from
it. In
this paper we introduce the concept of plausible reality. The aim of
plausible reality is also to generate virtual imagery that is
indistinguishable from reality, but without necessarily duplicating
it. The
key benefit of plausible reality over duplicating reality is that it
can be
done in real-time with simple computer vision techniques. We demonstrate
a
plausible reality system running real-time on an off-the-shelf PC.
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