principal axis-based correspondence between multiple cameras for people tracking
DESCRIPTION
Principal Axis-Based Correspondence between Multiple Cameras for People Tracking. Dongwook Seo [email protected] 2012.04.07. Overview. Detection of principal axes in a single camera. Motion segmentation and object classification - PowerPoint PPT PresentationTRANSCRIPT
Principal Axis-Based Correspon-dence between Multiple Cameras
for People Tracking
Dongwook [email protected]
2012.04.07
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Overview
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Detection of principal axes in a single cameraMotion segmentation and object classification
Using the vertical projection histogram to distinguish people from vehicles
1, , 1,height
yh x I x y x width
- I(x,y): binary image- height, width: the height and width of motion region
The spread of a vertical projection histogram
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1width
xwidth
x
h x h xSpread
h x
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Detection of Principal Axes
2argmin ,i ilL median D X l
Principal axis of an isolated personUsing the Least Median of Squares to determine the princi-pal axis of an isolated person
- : the perpendicular distance between the ith foreground pixel and axis
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Detection of Principal Axes(Cont.)Principal axes of people in group
(a) input image
(b) Detected foreground region
(c) Vertical projection histogram
(d) segmented individuals
(e) Principal axes
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Detection of Principal Axes(Cont.)Principal axes of people under occlusion
Using the color template-based method to segment people
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M X t M X t I X if X
P M X t if XP M X
P M X t if X
F
FF
- : color model of object i consist of a color variable - : the rgb color of each pixel X of object i- : the likelihood of object i being observed at pixel X
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TrackingThe construction of correspondence relationships between “tracked objects” in previous frames and “detected objects” in the current frame
To track people using Kalman filter: the state of a person
: the position of a person in the image plane: the velocity of a person
Using “ground-point” on the image plane for the position of individual
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Correspondence between multiple camerasHomography recovery
A homography is a 3 by 3 matrix H.
Consider a point in one image and in another image
11 12 13
21 22 23
31 32 1
h h hh h hh h
H
'11 12 13
'21 22 23
31 321 1 1
i i
i i
x h h h xy h h h y
h h
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Correspondence between multiple cameras(Cont.)
Geometrical relationship and correspondence likeli-hood
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Correspondence between multiple cameras(Cont.)
The function of correspondence likelihood
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1/2 1
, | |
1| 2 exp21| 2 exp2
i j i ji i ijs k s ks k sk
Ti ji i i ji i i jis ks s s ks s s ks
Ti ij j j ij j j ijk sk k k sk k k sk
L L p X Q p X Q
p X Q X Q X Q
p X Q X Q X Q
- : covariance matrixes (diagonal matrix-)- : covariance matrixes (diagonal matrix-)
The correspondence distance () for principal axis pairs
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,
i Tij i ji i jisk s ks s ks
j Tj ij j ijk sk k sk
i i j js k
D X Q X Q
X Q X Q
where
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Correspondence between multiple cameras(Cont.)
Correspondence between multiple camerasStep1. A list() of all possible correspondence pairs of princi-pal axes is created.Step2. For each pair in the pair list , it is checked whether pair satisfies the constraint
: Threshold to classify true or false correspondence pairsStep3. To find all possible pairing modes
, k: index of a paring mode Step4. The minimum sum of correspondence distance
All principal axis pairs in pair mode are the matched one.Step5. The pairs in pair set are labeled.
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ExperimentsResults on NLPR Database
Tracking and correspondence of multiple people with two cameras
# 3286
# 3297
# 3380
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Experiments(Cont.)Results on PETS2001 Database
Tracking and correspondence of multiple people with three cameras
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Experiments(Cont.)Tracking and correspondence
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Experiments(Cont.)Comparison
(a) Trajectory acquired using this paper and true data. E=3.2(b) Centroid trajectory and true data. E=5.8
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Experiments(Cont.)Comparison
- The white ones are acquired using this paper, and the black ones are centroid trajectories.(a) Trajectories in view 1.(b) Trajectories in view 2.
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ConclusionsFor matching people across multiple cameras
Using principal axis-based methodCamera calibration is not needed and there is less sensitivity to errors in motion detection.
Future workApplying this algorithms for non-planar ground surfaces
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Thank you!!!