"Person re
-
identification: a recent issue
for the
videosurveillance
community
and a technique for approaching it
Loris
Bazzani
Marco
Cristani
Modena,
17 maggio 2011
Before we start…
•
Download code and datasets for the exercises
(
iLIDS
,
VIPeR
, CAVIAR)
:
http://profs.scienze.univr.it/~bazzani/TMP/S4_SDALF_reid.zip
•
[opt.] Check out our CVPR 2010 paper:
http://www.lorisbazzani.info/papers/proceedings/FarenzenaetalCVPR10.pdf
•
[opt.] Check out the website:
http://www.lorisbazzani.info/code
-
datasets/sdalf
-
descriptor/
2
Outline of the lesson
1.
Person Re
-
identification
(few minutes…)
2.
A possible solution
:
SDALF, Symmetry
-
Driven Accumulation of
Local Features
(20 minutes…)
1.
Matlab
exercises
(~1 hour)
Person Re
-
identification
T = 1
T = 23
Different
overlapping
cameras
T = 222
T = 145
Same camera
•
Goal
:
Recognizing an
individual in
different
timings
Different
non overlapping
cameras
Person Re
-
identification
•
Issues
:
–
Many, you will see them in the exercises…
A possible solution: SDALF, Symmetry
-
Driven Accumulation of Local Features
•
Overview of the proposed descriptor:
STEP 2:
Chromatic
Feature
STEP 3:
Per
-
region
Feature
STEP 4:
Texture
Feature
STEP 0
-
1: Axes
of Symmetry
and Asymmetry
Descriptor
Accumulation
t
For each
body part
Step 0
–
Isolating the silhouette
•
We need to focus on the body of the person
•
We perform background
subtraction
or
•
We apply a statistical model of the
human appearance [
Jojic
et al.
2009]
Step 1
–
Axes of (A)
simmetry
•
We draw axes of
symmetry
and
asymmetry
•
Features near
the axes of
symmetry are
more reliable
Step 1
–
Axes of (A)
simmetry
BG subtraction using STEL generative model
Chromatic operator
Spatial covering operator
Step 2
-
Chromatic feature
•
For each part (no head), we compute a weighted color
histograms
•
HSV color space
•
“Gaussian Kernel” for each body part:
•
Low
-
weight to the background clutter
•
Robust to illumination changes, partial occlusions
Step 3
-
Per
-
region feature
•
Maximally Stable Color Region (MSCR)
detector
•
Detect “stable blobs”
•
Look at successive steps of an
agglomerative clustering of image
pixels
•
Covariant to affine transformations
Clustering of the detected blobs to
reduce the computational cost of the
matching
Step 4
-
Texture feature
•
Recurrent High
-
Structured Patches (RHSP) detector
Accumulation of features
•
Descriptor:
–
Single
-
shot: SDALF with only one image (no
accumulation)
–
Multi
-
shot: SDALF with multiple images
Testing the person re
-
identification
methods
A (probe)
B (gallery)
Pick a selection
Rank
Matching algorithm
•
Distance between two signatures
Bhattacharyya distance between HSV histograms
,
Distances between
blob descriptors
WHERE
How to evaluate
•
Cumulative Matching Characteristic (CMC)
curve
,
the
expectation of finding the correct
match in the top
n
matches
Ex. 1: The Datasets
•
Exercise 1
: take a look at the datasets and try
to find out the challenges of the re
-
id problem
17
For this, you can use the MATLAB file:
DEMO0_dataset.m
Ex. 2: SDALF
•
Exercise 2
: qualitative analysis of the SDALF
descriptor: display the weighted HSV hist.,
MSCR, RHSP
18
For this, you can use the MATLAB file:
DEMO1_SDALFextraction.m
Ex. 3: Cross
-
validation
•
Exercise 3
: try the cross
-
validation code
evaluating CMC, SRR and
nAUC
–
Compare
SvsS
and
MvsM
case
–
Vary the number of images for the
MvsM
case
19
For this, you can use the MATLAB file:
DEMO2_crossvalid.m
[set MAXCLUSTER=1 (
SvsS
) or >1 (
MvsM
)]
Ex 4: Matching
•
Exercise 4:
evaluate qualitatively the output of
the matching procedure
20
For this, you can use the MATLAB file:
DEMO2_crossvalid.m
[set
plotMatch
=1
]
And DEMO3_crossvalid.m
Take
-
home Message
•
Why this lesson?
–
To be able to use our system on new datasets
–
Compare your personal methods with SDALF
21
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