Dynamic Scenes by Image Sequence Analysis
description
Transcript of Dynamic Scenes by Image Sequence Analysis
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Dynamic ScenesDynamic Scenes
by by
Image Sequence AnalysisImage Sequence Analysis
Dynamic ScenesDynamic Scenes
by by
Image Sequence AnalysisImage Sequence Analysis
Jun ShenJun Shen2004
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Presentation schemePresentation scheme General presentation Dynamic scene analysis (DSA): a general view Motion detection
by background subtraction & by orthogonal moments 3D-model-based vehicle pose determination &
tracking Face tracking Gait tracking (Model-based tracking) Automatic gait recognition Learning & recognition of activity patterns by
fuzzy self-organizing Kohonen net Demonstration of results
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I. General I. General PresentationPresentation
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General framework of visual surveillance. . .
Fusion of Information from multiple cameras
Camera 1
Environment modeling
Motion segmentation
Object classification
Tracking
Behavior understanding and description
Personal identification
Camera n
PROCESSING
PROCESSING
I. General presentation
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II. Dynamic Scene II. Dynamic Scene
Analysis (DSA):Analysis (DSA):
A general viewA general view
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II. Dynamic Scene Analysis (DSA):II. Dynamic Scene Analysis (DSA):A general viewA general view
Low-level analysis
Motion detection Pose determination Hidden effect processing Moving object classification Tracking
II. DSA: A general view
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Motion detection methods– Background subtraction
– Temporal difference between successive frames
– Optic flow
– Matching: correlation, etc
– Frequency domain methods
Pose determinationII. DSA: A general view
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Moving object classificationMoving object classification
Classification based on geometric and radiometric properties of object– Shape– Size– Height-width ratio– Color– Texture– Features
Classification based on motion
II. DSA: A general view
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TrackingTracking
Tracking based on regions
Tracking based on moving contours
Tracking based on features
Tracking based on object models
II. DSA: A general view
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Behavior understanding and Behavior understanding and descriptiondescription
Finite state automate
Non-deterministic automate
Hidden Markov process model
Neural nets
Syntactic methods
….
II. DSA: A general view
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Person identification by gait analysis Person identification by gait analysis for video surveillancefor video surveillance
Model-based methods Statistical methods Characteristic-parameter-based methods Temporal-Spatial-motion-based methods Combination of gait analysis with other
biometrics methods
II. DSA: A general view
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Fusion of information from Fusion of information from multiple camerasmultiple cameras
Positioning of cameras Calibration of cameras Matching of objects from multi-camera Switching of cameras Fusion of information from multi-camera Hidden effect processing using multiple
cameras
II. DSA: A general view
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III. Motion detectionIII. Motion detection
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III. Motion detectionIII. Motion detection
Background subtraction Temporal Gaussian-Hermite moments
– Moments
– Orthogonal moments
– Gaussian-Hermite moments
– Motion detection by Gaussian-Hermite moments
III. Motion detection: 1. Background subtraction
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III.1. III.1. Motion detection Motion detection in color (or gray value) in color (or gray value)
image sequence image sequence by by
background subtractionbackground subtraction
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System overview
Based on background subtraction
FilteringBackground
image creation
Moving pixel
detection
Illumination change
elimination
Shadow eliminationLabeling
Input imagesequence
Moving objectsdetected
III. Motion detection: 1. Background subtraction
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Filtering & background image (c'tnd)A mobile object stops during a period > half the temporal W. size, It would be considered as static object and backgr'd updating will take moving object color.When it begins to move again, backgr'd image thus updated would disturb the detection of its motion (double moving objects
detected).
False moving object
III. Motion detection: 1. Background subtraction
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Solution –Color of moving pixels
not taken into account
in backgr'd updating.
–Distinguishing stopped
“mobile” objects from
real static objects.
–Comparison of present
& preceding positions
tells in motion or a
stopped mobile object.
Filtering & background image (c'tnd)
III. Motion detection: 1. Background subtraction
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Motion detection by background subtraction for color images
Difference between current frame & backgr'd
Current image
Background imageDiff
Difference imageR,B,G
Channels of Difference
Image
III. Motion detection: 1. Background subtraction
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Motion detection by b'gd subtraction (c'tnd)
Difference between current frame & backgr'd Segmentation of the difference color image
– Fuzzy segmentation of R,B,G channels separatelyAutomatic determination of threshold T,Fuzzy set “mobile pixels” by non-sym. m'ship function.
– Fuzzy segmentation with 3 channels together
III. Motion detection: 1. Background subtraction
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Motion detection by b'gd subtraction (c'tnd)
Difference between current frame & backgr'd Segmentation of the difference color image
– Fuzzy segmentation of R,B,G channels separatelyAutomatic determination of threshold T
T
hi
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Threshold by "Max. Distance"
Immobile Fuzzy set of mobile
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Difference between current frame & backgr'd Segmentation of the difference color image
– Fuzzy segmentation of R,B,G channels separately Automatic determination of threshold T, Fuzzy set “mobile pixels” by non-sym. m'ship
function.
– Fuzzy segmentation with 3 channels together
T c= dmax+ k/( dmax- dmin), (k>0)
dmax and dmin, max. & min. intensities.
+
(x)
x
Motion detection by b'gd subtraction (c'tnd)
III. Motion detection: 1. Background subtraction
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ATD (Automatic Threshold by max. distance)
Actual color frame Background image
Fuzzy M’ship fn
Automatic threshold by moment conservation method
Difference Image
ATD
B channel G channel ATD
Fuzzy M’ship fnFuzzy M’ship fn
Fuzzy deduction
Mobile pixel image
R channel
Fuzzy Segmentation of Difference ImageIII. Motion detection: 1. Background subtraction
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Elimination of false motion due to illumination change
Problem Bg'd image update using
preceding frames not fast adapted to illumination v.
Rapidness of bg'd adaptation depends on temporal window size & bg'd adaptation method.
Even auto-adaptation used, bg'd adapted to illumination change only after an accumulation of frames
III. Motion detection: 1. Background subtraction
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AND
Validated mobile pixels
Mobile pixels detected by background subtraction for
the current frame
Mobile pixels detected in
preceding frame
Mobile pixels detected by variation
in successive frames
OR
Diagram of false motion elimination
III. Motion detection: 1. Background subtraction
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Shadow Elimination Problem: Shadows of moving objects being of
almost the same motion as moving objects Importance of shadow elimination
Obtaining more precise description of moving objects
Center of gravity
III. Motion detection: 1. Background subtraction
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III.2. Motion detection by III.2. Motion detection by
orthogonal momentsorthogonal moments
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III.2. Motion detection by III.2. Motion detection by orthogonal moments orthogonal moments
III. Motion detection: 2. G-H moments
Moments
– Geometric, Legendre & Hermite moments
– Behavior in space & frequency domains
– Gaussian-Hermite (G-H) moments Motion detection by G-H moments Comparison with other methods Concluding remarks
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Geometric, Legendre & Hermite Geometric, Legendre & Hermite moments and their calculationmoments and their calculation
Geometric moments and their calculation 1D geometric moments Mn(x) at point x: Mn(x)= S(x+ t) tn dt n= 0, 1, 2, ...
2D geometric moments of a 2D image I(x, y):– Mm, n(x, y)= I(x+ u, y+ v) um vn du dv
Fast algorithms, such as Pascal Triangle. Explicit statistical signification. Functional analysis viewpoint: Signal projected onto
polynom. space, taking monomial functions as bases.
w
w
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1
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w
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w
III. Motion detection: 2. G-H moments
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Orthogonal Legendre moments
Using orthogonal bases:– Calculation could be reduced, – Error easier to estimate when limited proj. used, – Reconstruction simpler.
Orthogonal Legendre polynomials: (dn/ dxn) (x2- 1)n / (2n. n!) for x [-1, 1],
Pn(x) = {
0 otherwise.
III. Motion detection: 2. G-H moments
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Scaled Legendre polynomials: [(dn/ dxn) (x2- w2)n ]/ [(2 w)n. n!] for x [-w, w]
Ln(x) = { 0 otherwise.
n-th order orthogonal Legendre moment:
Mn(x) = S(x+ t) Ln(t) dt
= <L0(t), S(x+ t)> (inner product).
w
w
III. Motion detection: 2. G-H moments
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Recursive calculation of Legendre moments
The nth order orthogonal L. moments, calculated from window [x- w, x+ w], can be computed from (n- 1)th & (n- 2)th order L.M. : M0(x) = <L0(t), S(x+ t)> = S1(x+ w) - S1(x- w)
M1(x) = <L1(t), S(x+ t)> = [S1(x+ w) + S1(x- w)] - <L0(t), S1(x+ t)> / w
Mn(x) = <Ln(t), S(x+ t)> = <Ln-2(t), S(x+ t)> - [(2n- 1)/ w] <Ln-1(t), S1(x+ t)> , for n> 1with S0(t)= S(t) and Si(t)= Si-1(y) dy for i= 1, 2, 3, …
Si(t) easily calculated from Si-1(t) by recursive sum-box tech.
t
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2D Legendre moments
In 2D cases:
kx ky
Mp, q(x, y)= I(x+ t, y+ v) Lp(t) Lq(v) dt dv -kx -ky
Separable, decomposed into cascade of 1D calculation, by recursive algo.
III. Motion detection: 2. G-H moments
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Hermite moments Scaled Hermite polynomial
Pn(t)= Hn(t/)with Hn(t)= (-1)n exp (t2) (dn/ dtn) exp (-t2).
1D n-th order Hermite moment:Mn(x, S(x))= Pn(t) S(x+ t) dt n= 0, 1, ...
2D Hermite moments of an image I(x, y):Mp,q(x,y,I(x,y))= Hp,q(t/, v/) I(x+t, y+v) dt dv
with Hp,q (t/, v/)= Hp(t/) Hq(v/). Separable, calculated by cascade of 1D.
III. Motion detection: 2. G-H moments
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Behavior of geometric, Legendre & Hermite
Moments in space & frequency
domains Importance of behavior analysis
Behavior in space domain
Behavior in frequency domain
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Geometric moment base functions– Graphs of similar shapes, – Moments considered as projections onto base function
space, not efficient for diff.spatial modes. Hermite & Legendre mnt. base functions
– Many oscillations, depending on the order,– Extract efficiently characteristics of diff. spatial modes
(orthogonal polynomial of order n has n diff. zero-crossings).
Oscillations in Hermite bases much less important than Legendre ones (because the Hermite bases are not really orthogonal).
Same conclusion holds in 2D cases.
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Geometric moment base functions:– low-pass kernel, FT monotonically decreased.
Hermite moment base functions:– as order increased, max. FT position moves to right,
and more and more similar to a band-pass kernel. Legendre moment base functions:
– best band-pass characteristics except for very low orders. The higher the order is, the more to the right the pass-band moves.
L. moments separate characteristics in different frequency bands better than H. moments, which are in turn better than geometric ones.
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Gaussian-Hermite Moments Problem:Discontinuity in Geometric, H. & L. moment base at window boundary.How better represent local characteristics of (noisy) images? Solution: Smoothed orthogonal Gaussian-Hermite (G-H) momentsOrthogonal moments with Gaussian smoothing window function
Gaussian smoothing function g(x, )= (2 2) -1/2 exp (-x2/ 22) n-th order smoothed G-H moment:
Mn(x, S(x))=
Bn(t) S(x+ t) dt n= 0, 1, ...
with Bn(t)= g(t, ) Pn(t)Pn(t): scaled Hermite polynomial function of order n defined by
Pn(t)= Hn(t /)
with Hn(t)= (-1)n exp (t2) (dn/ dtn) exp (-t2)
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Property of G-H moments
Orthogonal moments.
Recursively calculated as follows: Mn(x, S(m)(x))= 2(n-1) Mn-2(x, S(m)(x)) + 2Mn-1(x, S(m+1)(x))
for m>=0 and n>= 2
with M0(x, S(m)(x))= g(x, ) * S(m)(x)) for m>= 0
M1(x, S(m)(x))= 2 d[g(x, )]/ dx * S(m)(x)) for m>= 0
and in particular,
M0(x, S(x)) = g(x, ) * S(x)
M1(x, S(x)) = 2 d[g(x, ) * S(x) ] / dx
where S(m)(x) dmS(x) / dx
m, S(0)(x) S(x).
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Comparison G-H moments better separate diff.
bands. Larger quality factor
Q= (Center freqency)/ (Effective bandwidth). G-H moments & G.-filtered deriv.:
– G-H moments: linear combinations of Gauss-filtered derivatives of signal.
– Construct orthogonal features from Gaussian-filtered derivatives.
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G.-H. moments & wavelet analysis Derivatives of Gaussians widely used as mother
wavelets, Different order derivatives of Gaussian filters
define different wavelets, Derivatives filtered by Gaussian filters of different
represent the decomposition of signal into wavelets.
Smoothed orthogonal Gaussian-Hermite moments offer a solution to construct orthogonal features from the wavelet analysis results.
III. Motion detection: 2. G-H moments
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2D orthogonal G-H moments
Mp, q(x, y, I(x, y)) =
G(t, v, ) Hp,q (t/, v/) I(x+ t, y+ v) dt dv
with Hp,q (t/, v/)= Hp(t/) Hq (v/),
scaled 2D Hermite polynomial.
Separable, cascade of 1D recursive algorithm.
III. Motion detection: 2. G-H moments
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Performance comparison: Sensibility to noise
Noise-free images and noisy ones with additional random noise,
Moment vectors (m0,0, m0,1, …, m0,5, m1,0, m1,1, …, m1,5),
Normalized distances between noise-free images and noisy ones.
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Orthogonality equivalence
To better understand the good performance of orthogonal moments in both spatial and frequency domains, we have
Orthogonality equivalence theorem
- Orthogonal moment base functions are not only orthogonal in spatial domain but also in frequency domain.
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Experimental verification Three different reference shape images: quadrilateral, hexagon and octagon. Noisy images: adding random noises of diff. standard deviations. Each shape characterized by 12 moments of orders (0,0), ..., (0,5), (1,0), ..., (1,5). Geometric, H. and L. moments are tested. Classification by comparing moment vector of noisy shape with the 3 ref. shapes.
III. Motion detection: 2. G-H moments
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Motion detection Motion detection by by Gaussian-Hermite Gaussian-Hermite
momentsmoments Why using G-H moments Motion detectiMotion detectionon using using G-H momentsG-H moments ResultsResults and comparison
– Comparison with differential methodsComparison with differential methods
– Comparison with Comparison with background background subtractionsubtraction
– Comparison withComparison with adaptive background
subtraction
III. Motion detection: 2. G-H moments
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Why using G-H moments?– Methods of motion detection in image sequence
Background-subtraction-based, including stochastic estimation of activation
Difficulty– Frame-to-frame illumination changes,
– Slowly moving and/or uninterested moving objects
– Calculation of adaptive background images demanding accumulation of a large number of images.
Based on temporal variation in successive images
Difficulty– Sensibility to noise
III. Motion detection: 2. G-H moments
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Advantages of using orthogonal G-H moments for motion detection– G-H moments: linear combinations of image
derivatives, permitting to detect image changes– Much smoother than other moments, therefore much
less sensitive to noises, facilitate moving object detection in noisy image sequences.
– Odd-order G-H moment base functions: linear combinations of odd order derivatives of Gaussian functions.
– Temporal G-H moments: composed of temporal image derivatives to detect moving objects in image sequences.
III. Motion detection: 2. G-H moments
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DetectiDetectingng moving target moving targets using s using G-H G-H moments of differenmoments of differentt orders orders
Given an image sequence Calculation of temporal G-H moments M1, M3 and M5
Fuzzy motion detection by moment image segmentation, using threshold by improved invariable-moment--method, using non-sym. Mship function for each point in moment images.
Membership function update by fuzzy relaxation: spatial relation between pixels in single and successive frames
Moving pixel decision
III. Motion detection: 2. G-H moments
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Comparison with differential methodsComparison with differential methods
Comparison with other methods
III. Motion detection: 2. G-H moments
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Comparison with Comparison with background background subtractionsubtraction
Test image sequence:illumination changed in some frames
Background subtraction method fails for illum. changed frames
G-H moments succeed
III. Motion detection: 2. G-H moments
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Comparison withComparison with adaptive adaptive background subtractionbackground subtraction
Adaptive back’d subtr’n improving motion
detection
Problem: back’d updating para. value choice,
depending on motion velocities
G-H moments: problems much better solved.
III. Motion detection: 2. G-H moments
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Example: an image sequenceExample: an image sequence
III. Motion detection: 2. G-H moments
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Motion detection result by G-H momentMotion detection result by G-H moment
III. Motion detection: 2. G-H moments
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Moving car trajectory (Moving car trajectory (Spline)Spline)
III. Motion detection: 2. G-H moments
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IV. 3D-model-based IV. 3D-model-based
Pose determination &Pose determination &
Tracking of vehicles Tracking of vehicles
IV. Pose and tracking
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System configurationSystem configuration
Camera model
Image sequence
3D vehicle model
Low-level video tracking
High-levelbehavior analysis
IV. Pose and tracking
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System frameworkSystem framework
New target
s?
Motion detection
Initialization
3D pose estimation
Model projection
Pose optimization Pose quality evaluation
Obstacle hiding analysis
NN
YY
Behavior analysis and semantic description
Image sequence Camera calibration Modeling
Low-level processing
High-level processing
IV. Pose and tracking
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Vehicle pose determinationProblems:
Detection of region of interest containing
a vehicle
Motion detection
Classification of moving objects
Determination of 3D pose of the vehicle
IV. Pose and tracking
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Known dataKnown data
ROI containing the vehicle on the image
3D model of the vehicle
Camera intrinsic and extrinsic parameters
Road surface plane constraint
Initial vehicle pose estimation
IV. Pose and tracking
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Pose quality evaluationPose quality evaluation
Model features:
Selected straight edge segments of the 3D model
Image features:
Edge points detected on the image
Quality based on PLS (Point to Line Segment) distance
IV. Pose and tracking
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Pose optimizationPose optimizationMake move 3D model in 3D space, from the
initial pose estimation to optimal pose3D model moves on the road surface plane
(Plan motion constraint):Translation on the road planeRotation around axis normal to road plane & passing through vehicle’s center of gravity
Weak projective projection hypothesis:Translation and rotation above independent on the image plane
DecompositionTranslation optimization + Rotation optimization
IV. Pose and tracking
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Translation optimizationTranslation optimization
For the projection Lp of the pth segment of 3D
model, define a subset of I:
Pose error function2
),(,,, )),(]/x([),(
p Ikj
pkjpkjpkj
p
LIDngngLIH
),(min),(| ,,, nkjn
pkjkjp LIDLIDIII
IV. Pose and tracking
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Determination of rotationDetermination of rotation
ZM
YMXM
OM
ZW
YWXW
OW
ZC
YC
XC OC
M: Reference system on the model objectW: Reference system in 3D worldC: Reference system on the camera
IV. Pose and tracking
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Rotation optimizationRotation optimization
After translation optimization
Searching in a small interval centered at
the estimated rotation angle
Take the angle that minimizes the pose error function
IV. Pose and tracking
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Hidden effect detection and Hidden effect detection and visible region determinationvisible region determination
Different types of hidden effectCase 1: Moving object hidden by backgroundCase 2: Moving object leaving or entering in
the vision fieldCase 3: Moving object hidden by other moving
objects Hidden effect detection Visible region determination
IV. Pose and tracking
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Experimental resultsExperimental results
IV. Pose and tracking
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Principle of TrackingPrinciple of Tracking Non-deformable solid object tracking
(Vehicles, …)
For the entire object:– Shape, size, color, ...– Estimation from motion
Solid objects with joints (Human body, etc)
For each part of the object: – Sub-model: shape, size, color, ...– Estimation from the motion
IV. Pose and tracking
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V. Face TrackingV. Face Tracking
70Overview of AlgorithmOverview of Algorithm
An
inp
ut
seq
uen
ce
Template Confidence Measure > Threshold
Y
Motion Detection
Set Search Region
Body-part Constraints
NFace
Detection
The first frame in an image sequence
Template Initialization
Template Matching
Template Updating
H Profile
V Profile
V. Face tracking
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VI. Gait TrackingVI. Gait Tracking
(Model-based tracking)(Model-based tracking)
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Gait TrackingGait Tracking(Model-based tracking)(Model-based tracking)
Pose optimization
Human body model
Current frame
Human body pose in the preceding
frame or
the initial pose
Pose estimation
Motion Model
Motion constraints
Tracking result
Application
Initialization Dynamic modelMotion model
Motion constraints
Search strategyHuman body model
Pose evaluation function
Motion synthesisGait recognition
VI. Gait tracking
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Model representation & LearningModel representation & Learning
- Geometric model of human body- Geometric model of human body
• Generalized cylinder model
• Motion parameters: 1021 ,,,,, yxP
VI. Gait tracking
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VII. Automatic Gait VII. Automatic Gait
RecognitionRecognition
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VII. Automatic Gait RecognitionVII. Automatic Gait Recognition
Gait: – Useful biometric feature for recognition – Attractive modality of human identification at
a great distance, for surveillanceApplication: automated person identification for
surveillance or monitoring systems in security-sensitive environments such as banks, parking lots and military bases.
Method based on Statistical Shape Analysis
VII. Gait recognition
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Advantages– The only perceivable biometric at a distance;– Not requiring proximal contact; – Easy to capture;– Difficult to conceal.
Disadvantages– A large amount of data;– Intermediate recognition accuracy;– Subject to some physical conditions such as drunkenness, pregnancy, and injuries involving joints.
Advantages and Disadvantages
VII. Gait recognition
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Camera
Gait image Sequence
Tracking
Background image
creation
Motion Detection
Gait Feature
Extraction
Classifier Database
Recognition Results
Monitoring Area
General framework of gait recognition
VII. Gait recognition
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VIII. VIII. Learning & Recognition Learning & Recognition of Patterns of Activity of Patterns of Activity
by by Fuzzy Self-Organizing Fuzzy Self-Organizing
Kohonen NetworkKohonen Network
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VIII. Learning & Recognition of VIII. Learning & Recognition of Patterns of Activity by Fuzzy Self-Patterns of Activity by Fuzzy Self-Organizing Kohonen NetworkOrganizing Kohonen Network• Activity understandingActivity understanding
in particular,in particular,
• Learning of activity patterns Learning of activity patterns
• Anomaly detectionAnomaly detection
• Activity predictionActivity prediction
VIII. Activity patterns
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General SchemaGeneral Schema
1. Moving target tracking
2. Trajectory coding
3. Activity recognition– Data acquisition
– Recognition structure
– Learning
– Recognition
VIII. Activity patterns
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•Recognition structure • Why Self-Organizing Kohonen Network?• Classical activity recognition systems?Classical activity recognition systems?
• Depending on predefined activity patternsDepending on predefined activity patterns• Non adaptable to changing environmentsNon adaptable to changing environments
Highly desirable to establish general approach Highly desirable to establish general approach of of activity recognition able to automatically activity recognition able to automatically generate activity modelsgenerate activity models..
Kohonen self-organizing topological mapKohonen self-organizing topological map‘‘Winner takes all’Winner takes all’
Using Using Fuzzy Fuzzy self-organizing Kohonen netself-organizing Kohonen net
VIII. Activity patterns
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Learning
• Training data: Training data: Set of training trajectories
VIII. Activity patterns
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Anomaly DetectionAnomaly Detection
• Detecting abnormal trajectoryDetecting abnormal trajectoryGiven a trajectory:
• We first look for the neuron that best matches it, which gives the class to which it is classified.
• If the Euclidean distance between the input trajectory code and the best matched neuron is greater than a threshold q, the activity represented by the trajectory is considered as unusual (unusual (abnormal).
VIII. Activity patterns
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Prediction of ActivityPrediction of Activity Given a part of a motion trajectory:Given a part of a motion trajectory:• Sampling this part to get a "sub-sample" vector
V.• Mismatching score between the sub-sample and
each neuron i by the Euclidean distance • The probability of each possible future motion
trajectory along which the object• According to the probabilities thus determined,
several future trajectories can be predicted with
probability. VIII. Activity patterns
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IX. IX. Demonstrations Demonstrations
of resultsof results
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Thank you!Address:
Jun ShenInstitut EGID - Bordeaux 31, Allée Daguin33607 Pessac cedexFRANCE
Email: [email protected]
Phone: (+33) 5 57 12 10 26Fax: (+33) 5 57 12 10 01