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![Page 1: "Person re-identification: a recent issue for the videosurveillance community and a technique for approaching it Loris Bazzani Marco Cristani ? Modena,](https://reader035.fdocuments.us/reader035/viewer/2022070408/56649e6b5503460f94b68e49/html5/thumbnails/1.jpg)
"Person re-identification: a recent issue for the videosurveillance community
and a technique for approaching it
Loris BazzaniMarco Cristani
?Modena, 17 maggio 2011
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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…)
3. Matlab exercises (~1 hour)
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Person Re-identification
T = 1 T = 23
Different overlapping cameras
T = 222T = 145
Same camera• Goal:
Recognizing an individual in different timings
Different non overlapping cameras
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Person Re-identification• Issues:
– Many, you will see them in the exercises…
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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 AsymmetryDescriptor
Accumulation
t
For each body part
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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]
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Step 1 – Axes of (A)simmetry
• We draw axes of symmetry and asymmetry
• Features near the axes of symmetry are more reliable
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Step 1 – Axes of (A)simmetry
BG subtraction using STEL generative model
Chromatic operator Spatial covering operator
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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
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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
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Step 4 - Texture feature
• Recurrent High-Structured Patches (RHSP) detector
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Accumulation of features
• Descriptor:– Single-shot: SDALF with only one image (no
accumulation) – Multi-shot: SDALF with multiple images
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Testing the person re-identification
methodsA (probe) B (gallery)
Pick a selection Rank
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Matching algorithm
• Distance between two signatures
Bhattacharyya distance between HSV histograms,
Distances between blob descriptors
WHERE
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How to evaluate
• Cumulative Matching Characteristic (CMC) curve, the expectation of finding the correct match in the top n matches
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Ex. 1: The Datasets
• Exercise 1: take a look at the datasets and try to find out the challenges of the re-id problem
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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
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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
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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
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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
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Questions?