"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/
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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
• Goal:
Recognizing an
individual in
different
timings
Same camera
T = 145
T = 222
Different
overlapping
cameras
T=1
T = 23
Different
non overlapping
cameras
Person Re-identification
• Issues:
– Many, you will see them in the exercises…
A possible solution: SDALF, SymmetryDriven Accumulation of Local Features
• Overview of the proposed descriptor:
STEP 0-1: Axes
of Symmetry
and Asymmetry
STEP 3:
Per-region
Feature
For each
body part
Descriptor
Accumulation
t
STEP 2:
Chromatic
Feature
STEP 4:
Texture
Feature
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
Chromatic operator
Spatial covering operator
BG subtraction using STEL generative model
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
WHERE
,
Bhattacharyya distance between HSV histograms
Distances between
blob descriptors
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
For this, you can use the MATLAB file:
DEMO0_dataset.m
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Ex. 2: SDALF
• Exercise 2: qualitative analysis of the SDALF
descriptor: display the weighted HSV hist.,
MSCR, RHSP
For this, you can use the MATLAB file:
DEMO1_SDALFextraction.m
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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
For this, you can use the MATLAB file:
DEMO2_crossvalid.m
[set MAXCLUSTER=1 (SvsS) or >1 (MvsM)]
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Ex 4: Matching
• Exercise 4: evaluate qualitatively the output of
the matching procedure
For this, you can use the MATLAB file:
DEMO2_crossvalid.m
[set plotMatch=1]
And DEMO3_crossvalid.m
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Take-home Message
• Why this lesson?
– To be able to use our system on new datasets
– Compare your personal methods with SDALF
Questions?
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