1 formation et analyse d’images session 8 daniela hall 14 november 2005
Post on 18-Dec-2015
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TRANSCRIPT
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Course Overview
• Session 1 (19/09/05)– Overview– Human vision – Homogenous coordinates– Camera models
• Session 2 (26/09/05)– Tensor notation– Image transformations– Homography computation
• Session 3 (3/10/05)– Camera calibration– Reflection models– Color spaces
• Session 4 (10/10/05)– Pixel based image analysis
• 17/10/05 course is replaced by Modelisation surfacique
3
Course overview
• Session 5 + 6 (24/10/05) 9:45 – 12:45– Contrast description– Hough transform
• Session 7 (7/11/05)– Kalman filter
• Session 8 (14/11/05)– Tracking of regions, pixels, and lines
• Session 9 (21/11/05)– Gaussian filter operators
• Session 10 (5/12/05)– Scale Space
• Session 11 (12/12/05)– Stereo vision – Epipolar geometry
• Session 12 (16/01/06): exercises and questions
4
Session overview
1. Tracking of objects
2. Architecture of the robust tracker
3. Tracking using Kalman filter
4. Tracking using CONDENSATION
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Robust tracking of objects
Trigger regions
Detection New targets
List of targets
PredictList of predictions
Correct
Detection
Measurements
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Tracking system
• Tracking system: detects position of targets at each time instant (using i.e. background differencing)
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Tracking system
• Supervisor– calls image acquisition, target observation and detection in a cycle
• Target observation module– ensures robust tracking by prediction of target positions using a Kalman
filter• Detection module
– verifies the predicted positions by measuring detection energy within the search region given by the Kalman filter
– creates new targets by evaluating detection energy within trigger regions• Parameters
– noise threshold, detection energy threshold, parameters for splitting and merging
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Detection by background differencing
• I=(IR,IG,IB) image, B=(BR,BG,BB) background • Compute a binary difference image Id, where all pixels that have a
difference diff larger than the noise threshold w are set to one.
• Then we compute the connected components of Id to detect the pixels that belong to a target.
• For each target, we compute mean and covariance of its pixels. The covariance is transformed to width and height of the bounding box and orientation of the target.
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Real-time target detection
• Computing connected components for an image is computationally expensive.
• Idea:– Restrict search of targets to a small number of search
regions.
• These regions are:– Entry regions marked by the user– Search region obtained from the Kalman filter that
predicts the next most likely position of a current target.
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Background adaption to increase robustness of detection
• In long-term tracking, illumination of a scene changes. Image differencing with a static background causes lots of false detections.
• The background is updated regularily by
• t time, α=0.1 background adaption parameter• Background adaption allows that the background
incorporates slow illumination changes.
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Example• Detection module
• Parameters: detection energy threshold– energy threshold too high: targets are missed or targets are split– energy threshold too low: false detections
• Problem: energy threshold depends on illumination and target appearance
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Session overview
1. Tracking of objects
2. Architecture of the robust tracker
3. Tracking using Kalman filter
4. Tracking using CONDENSATION
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Tracking
• Targets are represented by position (x,y) and covariance.
• A first order Kalman filter is used to predict the position of the target in the next frame.
• The Kalman filter provides a ROI where to look for the target. ROI is computed from the a posteriori estimate xk and from the a posteriori error covariance Pk
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Example: Tracking bouncing ball
• Specifications:– constant background– colored ball
• Problems:– noisy observations– motion blur– rapid motion changes
Thanks to B. Fisher UEdin for providing slides and figures of this example. http://homespages.inf.ed.ac.uk/rbf/AVAUDIO/lect8.pdf
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Ball physical model
• Position zk = (x, y)
• Position update zk = zk-1 + vk-1Δt
• Velocity update vk = vk-1+ak-1Δt
• Acceleration (gravity down) ak=(0,g)T
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Robust tracking of objects
• Measurement
• State vector
• State equation
• Prediction
• State control
Tk
kkk
kkk
Tk
k
tgBu
BuxAx
HvHxz
yxyxx
y
xz
),0,0,0(
,ˆˆ
0010
0001,
',',,
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Robust Tracking of objects
• Measurement noise error covariance
• Temporal matrix
• Process noise error covariance
• a affects the computation speed (large a increases uncertainty and therefore the search regions)
IQ
t
t
A
R
k
k
01.0
1000
0100
010
001
046.0005.0
005.0285.0
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Kalman filter analysis
• smoothes noisy observations
• dynamic model fails at bounce and stop
• could estimate ball radius
• could plot a boundary of 95% likelihood of ball position (the boundary would grow when the fit is bad).
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Session overview
1. Tracking of objects
2. Architecture of the robust tracker
3. Tracking using Kalman filter
4. Tracking using CONDENSATION
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Tracking by CONDENSATION
• CONDENSATION: Conditional Density Propagation. Also known as Particle Filtering.
Ref: M.Isard and A. Blake: CONDENSATION for visual tracking, Int Journal of Computer Vision, 29(1),1998.http://www.robots.ox.ac.uk/%7Econtours/
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CONDENSATION tracking
• Keeps multiple hypotheses
• updates using new data
• selects hypotheses probabilistically
• copes with very noisy data and process state changes
• tunable computation load (by choosing number of particles).
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CONDENSATION algorithm
• Given a set of N hypotheses at time k Hk={x1,k, ... , xN,k} with associated probabilities {p(x1,k), ..., p(xN,k)}
• Repeat N times to generate Hk+1– 1. randomly select a hypothesis xu,k from Hk with p(xu,k)– 2. generate a new state vector sk from a distribution centered at xu,k
– 3. get new state vector using dynamic model xk+1=f(sk) and kalman filter.
– 4. evaluate probability p(zk+1|xk+1) of observed data zk+1 given state xk
– 5. use bayes rule to get p(xk+1|zk+1)
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Why does condensation tracking work?
• many slightly different hypotheses suggests that maybe we find one that fits better.
• dynamic model allows to switch between different motion models – Motion models of bouncing ball: bounce,
freefall, stop
• sampling by probability weeds out bad hypotheses
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Tracking of bouncing ball
1. Select 100 hypotheses xk with probabilities p(xk)
2. use estimated covariance P() to create state samples sk
3. define a situation switching model
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Tracking of bouncing ball
• If in STOP situation: y'=0• If in BOUNCE: x'=-0.7x', also add some random
y' motion, y'=y'+r.• If in FREEFALL: use freefall motion model.
y'=gΔt and x'=x'+r• then use Kalman filter for predicting ^xk
• 4. estimate hypothesis goodness by 1/||Hxk – zk||2
• p(xk) is estimated from the goodness by normalization.