"Person re-identification: a recent issue

molassesitalianAI and Robotics

Nov 6, 2013 (3 years and 10 months ago)

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"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

Questions?