Design Me hodology o Face De ec ion Accele a ion
Lau en iu Acasand ei
Ins i u o de Mic oelec onica de Se illa
IMSE-CNM-CSIC
Se illa, Spain
lau en [email protected]
Angel Ba iga
Ins i u o de Mic oelec onica de Se illa/Uni . Se illa
IMSE-CNM-CSIC/ Uni . Se illa
Se illa, Spain
[email p o ec ed]
Abs ac —A design me hodology o accele a e he ace
de ec ion o embedded sys ems is desc ibed, s a ing om high
le el (algo i hm op imiza ion) and ending wi h low le el
(so wa e and ha dwa e codesign) by add essing he issues and
he design decisions made a each le el based on he pe o mance
measu emen s and sys em limi a ions. The implemen ed
embedded ace de ec ion sys em consumes e y li le powe
compa ed wi h he adi ional PC so wa e implemen a ions
while main aining he same de ec ion accu acy. The p oposed
ace de ec ion accele a ion me hodology is sui able o eal ime
applica ions.
Keywo ds— ace de ec ion; embedded sys em; design
me hodology; ha dwa e & so wa e codesign
I. INTRODUCTION
Face de ec ion is an impo an aspec o biome ics[1],
ideo su eillance and human compu e [2] in e ac ion.
De ec ion sys ems equi e huge compu a ional and memo y
esou ces due o he complexi y o de ec ion algo i hms. A
so wa e de ec ion ealiza ion implemen ed on a low speed,
low esou ce, low powe SoC (Sys em on a Chip) i is no
e icien . Ins ead a So wa e-Ha dwa e Codesign app oach can
be used o build ha dwa e accele a o s o mos compu a ional
consuming pa s o de ec ion algo i hms.
Real ime ace de ec ion applica ion equi es a high amoun
o mul iplie s and memo y esou ces unning a high speed.
Due o high amoun o esou ces and high speed equi emen s a
so wa e implemen a ion o he ace de ec ion algo i hm is no
easible o a low speed, low esou ce, low powe SoC.
Recen ly he e ha e been se e al ha dwa e ealiza ions ha
accele a e he ace de ec ion p ocess by pa allelizing he
de ec ion algo i hm. Thus some o he p oposals a e o
speci ic ha dwa e ealiza ions on ASIC [3] while o he s a e
implemen ed on FPGA [4-6]. Using he So wa e-Ha dwa e
Codesign app oach we can accele a e he de ec ion p ocess by
eplacing, wi h e y as dedica ed ha dwa e, some pa s o he
so wa e ha usually consumes la ge amoun o clock cycles
and/o memo y du ing he un ime. In [7] se e al pa s o he
Viola-Jones de ec ion mechanism ha e been pa allelized in
o de o un e icien ly on a GPU a chi ec u e.
The pu pose o his communica ion is o desc ibe a design
me hodology o accele a ing ace de ec ion applica ions.
Taking in o conside a ion ha he OpenCV lib a y comes wi h
wo baseline applica ions LBP (Local Bina y Pa e n) and
Viola-Jones o ideo de ec ion, we ha e chosen Viola-Jones
o de eloping a ace de ec ion applica ion o embedded
sys ems. The same design me hodology is common o bo h
algo i hms. The LBP and Viola-Jones ha e he same de ec ion
mechanism, he only di e ence being in how o encode/decode
he ea u es om an image.
II. FACE DETECTION TECHNIQUE
The ace de ec ion echnique is based on he ace de ec ion
amewo k p oposed by Viola-Jones [8]. The p oposed
amewo k is capable o p ocessing images ex emely apidly
while achie ing high de ec ion a es. The speed o he ace
de ec ion amewo k elies on h ee impo an key componen s.
Fi s ly, he image is ans o med in o “In eg al Image” which
allows he ea u es used by he de ec o o be compu ed e y
quickly. Secondly, he used classi ie is simple and e icien
which is build using he AdaBoos lea ning algo i hm [9] o
selec a small numbe o c i ical isual ea u es om a e y
la ge se o po en ial ea u es. And hi dly, he classi ie is
o med by combining weak classi ie s in a “cascade” which
allows backg ound egions o he image o be quickly
disca ded while spending mo e compu a ion on p omising
ace-like egions.
Viola-Jones echnique is based on explo ing he image by
means o a window looking o ea u es. This window is scaled
o ind aces o di e en sizes. The sys em a chi ec u e is based
on a cascade o de ec o s. The i s s ages consis o simple
de ec o s, e y as and low cos , ha allows o elimina e hose
windows ha do no con ain aces. In he successi e s ages he
complexi y o de ec o s a e inc eased in o de o make a mo e
de ailed analysis o ea u es. A ace is de ec ed only i i makes
i h ough he en i e cascade.
The Haa -like ea u es used by he classi ie consis o
ec angula a eas whose p ocessing equi es simple
a i hme ical ope a ions. The calcula ion is based on he sum o
he pixels o each ec angula egion weighed by a weigh . A
all scales, hese ea u es o m he “ aw ma e ial” ha will be
used by he de ec o . The se o ec angle ea u es in he image
is qui e la ge and o e comple e, so o educe ha numbe
applies he AdaBoos lea ning algo i hm [9]. The Viola-Jones
classi ie employs AdaBoos a each node in he cascade o
lea n a high de ec ion a e a he cos o low ejec ion a e
mul i ee (mos ly mul is ump) classi ie a each node o he
cascade.
To acili a e he p ocessing o he ea u es he ope a ions
a e no made on he o iginal image bu on an in eg al image.
The e o e he de ec ion algo i hm equi es a p ep ocessing s ep
ha calcula es his in eg al image. The in eg a ion o he image
consis s o adding o each pixel he alues o he p e ious
pixels.
III. DESIGN METHODOLOGY
The s a ing poin o he design me hodology o he
embedded sys em is he OpenCV’s Viola-Jones baseline ace
de ec ion applica ion. OpenCV (Open Sou ce Compu e
Vision), s a ed by In el in 1999, is a lib a y o p og amming
unc ions o eal ime compu e ision [10]. OpenCV is
eleased unde a BSD license and hence i is ee o bo h
academic and comme cial use. I is w i en in C/C++ and was
designed o compu a ional e iciency and wi h a s ong ocus
on eal- ime applica ions.
The hos a ge o he p oposed ace de ec ion sys em is an
embedded en i onmen based on LEON3 AMBA Bus
p ocesso . The LEON3 is a syn hesizable VHDL so co e o a
32-bi p ocesso complian wi h he SPARC V8 a chi ec u e
[11]. The p ocesso is highly con igu able, and pa icula ly
sui able o sys em-on-a-chip (SOC) designs. The ull sou ce
code is a ailable unde he GNU GPL license. The p ocesso
con ols and execu es he majo i y o so wa e applica ion asks
while a speci ic IP (In ellec ual P ope y) module accele a e
only hose asks ha equi e a high numbe o clock cycles.
The design low is based on ou s ages, as shown in Figu e
1. In he i s s age an adap a ion o he so wa e applica ion o
execu e on he embedded sys em has been made. In he nex
s age an analysis o he new embedded so wa e applica ion is
pe o med, in o de o de ec "bo lenecks" and hose asks ha
a e sui able o accele a e h ough ha dwa e implemen a ion. In
he hi d phase, as esul o he p e ious analysis, solu ions o
op imize and accele a e some o he asks o he ace de ec ion
p ocess will be p oposed. The ou h s age consis s in he
ha dwa e implemen a ion o hose pa s ha consume a many
esou ces and ha e la ge un imes. The idea is o o load o he
ha dwa e he unc ions wi h a high deg ee o p ocessing and o
pa allelize he execu ion o he de ec ion algo i hm. Wi h his
he ace de ec ion p ocess can be d as ically accele a ed.
Fig. 1. Design me hodology
A each s age o he design p ocess he pe o mance o he
ace de ec ion sys em is analyzed, in e ms o speed and
de ec ion accu acy. The accu acy o ace de ec ion p ocess is
analyzed using ROC (Recei e Ope a ing Cha ac e is ic)
cu es. The ROC cu e o a gi en ace de ec o shows i s
pe o mance as a ade-o be ween he alse accep ance a e
and he ace de ec ion a e by a ying i s disc imina ion
c i e ion (e.g. a h eshold pa ame e ).
IV. EMBEDDED SOFTWARE FACE DETECTION
IMPLEMENTATION
Viola and Jones o ganized each boos ed classi ie g oup
in o nodes o a ejec ion cascade. Each o he nodes con ains an
en i e boos ed cascade o g oups o decision s umps (o ees)
ained on he Haa -like ea u es om aces and non aces (o
o he objec s he use has chosen o ain on). Typically, he
nodes a e o de ed om leas o mos complex so ha
compu a ions a e minimized (simple nodes a e ied i s ) when
ejec ing easy egions o he image. Typically, he boos ing in
each node is uned o ha e a e y high de ec ion a e (a he
usual cos o many alse posi i es). When aining on aces, o
example, almos all (99.9%) o he aces a e ound [12] bu
many (abou 50%) o he non aces a e e oneously “de ec ed”
a each node. Bu his is sa is ac o y because using, as an
example, 20 nodes will s ill yield a ace de ec ion a e ( h ough
he whole cascade) o 0.99920 ≈ 98% wi h a alse posi i e a e
o only 0.520 ≈ 0.000001%!
Du ing algo i hm execu ion, a sea ch window o di e en
sizes is swep o e he o iginal image. In p ac ice, 70–80% o
non aces a e ejec ed in he i s wo nodes o he ejec ion
cascade, whe e each node uses abou en decision s umps. This
quick and ea ly “a en ional ejec ” as ly speeds up ace
de ec ion.
The Haa -like ea u es a e ained o be applied o a
e alua ing ec angula window o 20x20 pixel. Fo o he
dimensions o he e alua ing window he Haa -like ea u es
mus be scaled co espondingly. The OpenCV so wa e
implemen a ion o ace de ec ion consis s o 22 cascade
de ec o s, con aining 2135 Haa like ea u es.
The i s ask o So wa e-Ha dwa e Codesign was adap ing
and op imizing he OpenCV baseline applica ion o an
embedded en i onmen . We ha e conside ed ha he majo i y
o embedded en i onmen s a e capable o unning C/C++
applica ions wi h o wi hou Ope a ing Sys em (OS) suppo .
This means ha he esul ing applica ion code has o be
compa ible o bo h C, C++ compile s and in he same ime
pla o m independen .
Ano he conside a ion made is he ac ha mos o he SoC
ha e no loa ing poin suppo . Fo i , he esul ing applica ion
uses in ege ope a ions ins ead o loa ing poin ope a ions in
o de o p ese e he gene ali y o he applica ion o he
embedded sys em wo ld. An impo an momen in his s ep
was inding an accep able scaling coe icien o he loa ing
poin a iables and da a o in ege a iables and da a. A e
ying di e en alues and compa ing he esul ed in ege
applica ion wi h he loa ing poin applica ion we ound ha by
scaling wi h 20 bi s (p ecision o 20 bi s o he loa ing poin
decimals) he in ege and loa ing poin applica ions ob ain
Pe o mance and de ec ion
accu ac
ya
nalysis
OpenCV So wa e Face
De ec ion Applica ion
Embedded So wa e
Implemen a ion
Embedded So wa e
Anal
y
sis
Algo i hm Op imiza ion
Ha dwa e accele a ion
iden ical esul s. Also he loa ing poin squa ed oo unc ion
necessa y o calcula e a iance o he e alua ing window was
eplaced wi h a as in ege squa ed oo e sion.
In he end i was ob ained a ace de ec ion s and-alone
applica ion compa ible wi h C/C++, using only in ege ype
ope a ions and da a.
V. EMBEDDED SOFTWARE FACE DETECTION ANALYSIS
The nex s ep in applica ion de elopmen was analyzing
di e en modes o de ec ion in o de o ind he un ime
bo lenecks and op imize he de ec ion. Fo he embedded
a ge we ha e decided o use he de ec ion mode whe e he
de ec o (Haa -like ea u e) is scaled and he bigges egions
con aining aces a e sea ched wi hin an image. In his mode he
de ec ions s a s wi h he bigges e alua ion window and
bigges e alua ion s ep and p og essi ely, he window oge he
wi h he e alua ion s ep a e dec eased un il a egion con aining
a ace is de ec ed. In he case ha a egion wi h ace o mul iple
aces is de ec ed, he a en ion o he de ec o concen a es in
ha egion.
The ained classi ie cascade (Haa -like ea u e) is
p o ided by OpenCV in an XML ile o ma . Using his XML
o ma in an embedded sys em will p oduce memo y and un
ime o e head. In o de o a oid he o e head we ha e
de eloped an applica ion ha ecei es a XML ile, in e p e s
he da a and sa es i in a simple o ma o a C heade ile. The
esul ed embedded applica ion can be compiled wi h he
cascade o classi ie o he da a can be ansmi ed du ing he
execu ion o he applica ion ia an app op ia e in e ace.
In o de o ob ain ele an insigh abou which pa s (o
unc ions) o he ace de ec ion p og am a e aking mos o he
execu ion ime we enable -gp lag in he Eclipse p ojec
compila ion op ions in o de o gene a e p o iling in o ma ion
ha can be in e p e ed wi h GNU gp o ool. This ool pe mi s
one o lea n whe e he p og am spends i s ime and he unc ion
calling ee du ing he execu ion. I can also ell which
unc ions a e being called mo e o less han a e expec ed.
Table I shows he ob ained esul s. As we can see he
unc ion Se Ma Ze o() uses 24.64% o he execu ing ime e en
su passing he ime spen applying he cascade Haa like-
ea u es (20.16%) o he en i e image. The unc ion
Se Ma Ze o() is used o se o ze o all he elemen s o a
empo a y ma ix ha ing he same dimension o he image. In
his ma ix he op le coo dina es o a de ec ed ace a e
lagged wi h alue 1. In his mode he de ec ions s a s wi h he
bigges e alua ion window and bigges s ep and p og essi ely,
he window and he s ep a e dec eased un il a egion con aining
a ace is de ec ed. The de ec ion is done in wo s eps:
 Fi s S ep: The image is scanned wi h he e alua ion
window by applying only he i s wo Haa -like ea u e
s ages in o de o apidly de ec egions con aining
po en ial aces. I a egion is ound o ha e a po en ial
ace hen he coo dina es a e se o alue one in he
empo a y ma ix.
 Second s ep: Each po en ial ace (s a ing wi h hei
coo dina es) om he empo a y ma ix is e alua ed
wi h he emaining Haa -like ea u e s ages. I a ue
ace is de ec ed hen he coo dina es, wid h and heigh
a e s o ed in a lis . A he end o he second s ep, he
empo a y ma ix is se o ze o in p epa a ion o he
nex image whe e he e alua ing window has smalle
dimensions.
We can imp o e he speed by no using he unc ion
Se Ma Ze o() a he end o he second s ep and ins ead each
ime a e we ha e de ec ed a ace du ing he second s ep and
ha ace is s o ed in o a lis , we se o ze o he coo dina es
inside he empo a y ma ix. A e applying his change, he
de ec ion ime is imp o ed wi h 24.64 %.
TABLE I. ANALYSIS OF DETECTION SYSTEM BOTTLENECKS
Time % # calls Func ion name
24.64 17
Se Ma Ze o()
20.16 3201
RunHaa Classi ie CascadeEmbedd()
14.81 16
Se ImagesFo Haa Classi ie CascadeEmbedd()
13.39 1
In eg al()
11.35 1
LinkDa aToEmbeddClassi ie Cascade()
4.22 511
HResizeLinea ()
3.11 262144
sa u a e_ucha ()
2.62 512
VResizeLinea ()
1.80 296384
sum_elem_p ()
1.10 3201
isq 64()
VI. EMBEDDED FACE DETECTION OPTIMIZATION
The OpenCV ace de ec ion baseline applica ion
implemen s de ec ion in wo dis inc modes:
Mode 1: Face de ec ion by scaling he image. In his mode
he image is scaled using in e pola ion un il i eaches a
p ede ined minimal dimension. Each ime he image is scaled
he wo in eg al images (no mal= x and squa ed= 2
x),
needed o a iance, a e ecalcula ed o he scaled image. The
sea ch window has ixed dimension du ing he de ec ion
p ocess.
Mode 2: Face de ec ion by scaling he classi ie s. In his
mode he in eg al images(no mal= x and squa ed= 2
x),
needed o a iance, a e calcula ed only once o he o iginal
image bu he Haa -Like ea u es o m he classi ie a e scaled
p og essi ely un il hei dimensions a e close o he dimension
o he o iginal window. This mode lacks he in e pola o used
in mode 1. The sea ch window has a a iable dimension du ing
de ec ion p ocess.
In bo h de ec ion mode he Haa -like ea u es componen s
(weigh s and dimensions) a e scaled p opo ionally wi h he
dimensions o sea ch window. Tha means o a sea ch window
o dimension WxH, he weigh o each ec angle o ming he
Haa -like ea u es a e scaled wi h WxH. In he sea ch window,
he sum o each applied Haa -like ea u e is calcula ed using:


 3
1I
scaled
I
I
Sum Weigh A eaeHaa Fea u (1)
A ea ep esen s he sum o all pixels inside a componen
and I=1, 2 o 3 ep esen s he numbe o componen s o ha
Haa -like ea u e. In o de o de e mine he nex weigh alue
o he s age sum, each Haa Fea u eSum is compa ed wi h each
no malized h eshold o he espec i e Haa -like ea u e as:








no m
J
Sum
J
Weigh
J
no m
J
Sum
J
Weigh
J
Th eseHaa Fea u i S ageS ageSum
Th eseHaa Fea u i S ageS ageSum
S ageSum ,
,
1
2
(2)
whe e J=[1…2135] ep esen s he Haa -like ea u e indexes
in a s age and Th es
J
no m=σTh esJ
Haa Fea u e (σ is s anda d
de ia ion o he windows sea ch a ea).
I we do no scale he Haa -like ea u e weigh s and adjus
he a iance compu a ion by using he o mula

)( 2
22   xxHW
adjus ed

, i esul s ha he numbe o
a i hme ic ope a ions (di ision, mul iplica ion) and memo y
accesses a e dec eased subs an ially. This will make he
algo i hm pe o m as e due o a educed numbe o ope a ions
needed o compu a ion o he adjus ed a iance o he sea ch
window [13]. Figu e 2 shows he p oposed op imiza ion o he
de ec ion algo i hms in he wo de ec ion modes (scaling he
image and scaling he classi ie s).
Fig. 2. P oposed ace de ec ion accele a ion algo i hm
In o de o compa e he pe o mance o he OpenCV 2.2
baseline ace de ec ion and he accele a ed Viola-Jones
algo i hm, and o analyze he in luence o he con igu a ion
pa ame e s, bo h implemen a ions ha e been compiled and
speed op imized o 64 bi Win7 OS using Visual S udio 2010
P o esional edi ion. The e i ica ion PC has a Pen ium Dual-
Co e CPU T4300, wi h L1 cache o 128KB, L2 uni ied cache
1024KB and he OS is Windows 7 Home Edi ion 64 bi s.
Bo h implemen a ion (OpenCV’s implemen a ion and
accele a ed Viola-Jones e sion) ha e ecei ed he same es
VGA (640x480) images and he same con igu a ion
pa ame e s. In Table II he con igu a ion ha e di e en scale
ac o (s ) and minimum sea ch window dimensions (swd):
s =1.1 and swd=20x20 (Con 1); s =1.2 and swd=20x20 (Con
2); s =1.1 and swd=30x30 (Con 3).
TABLE II. P
ERFORMANCES OF
O
PEN
C
V AND ACCELERATED
V
IOLA
-
J
ONES IMPLEMENTATION FOR DIFFERENT PARAMETERS
Mode 1. Img scaling Mode 2. Haa scaling
OpenCV
Baseline
Op imized
e sion
OpenCV
Baseline
Op imized
e sion
Con 1
speed 708.8 ms
1.41 FPS
185.7ms
5.38FPS
843.9 ms
1.18FPS
188.1ms
5.3FPS
Sea ch
windows 602348 599816 697582 631343
Con 2
speed 409.5 ms
2.44FPS
164.8ms
6.06FPS
479.7ms
2.08FPS
140.1ms
7.1FPS
Sea ch
windows 354321 353935 381474 364635
Con 3
speed 348.1ms
2.87FPS
99.4ms
10FPS
456.7 ms
2.18FPS
130.6ms
7.6FPS
Sea ch
windows 352718 351184 402519 332917
As he p oposed implemen a ion has kep he con ol
mechanism o sea ch windows iden ical wi h he one o m he
OpenCV baseline i is di icul o make a compa ison wi h
p e ious wo k done in accele a ing de ec ion due o he lack o
se up in o ma ion and how many sea ch windows a e
e alua ed. The e is one excep ion Cho e al [6] whe e hey use
mode1 wi h a scaling ac o o 1.2, minimal sea ch window
dimension o 20x20 and he sea ch window is applied wi h a
e ical ho izon al s ep o 1. Table III shows he compa ison
esul s.
TABLE III.
P
ERFORMANCE OF THE
O
PEN
CV
BASELINE AND
ACCELERATED
V
IOLA
-J
ONES USING THE
C
HO ET AL
.[6]
SETUP
Implemen a ion Mode1-Image scaling
Speed Sea ch Windows
OpenCV 974.3 ms
1.02FPS 881484
Accele a e Viola-Jones 451.4 ms
2.21 FPS 880585
As shown in Tables II and III he numbe o sea ch
windows depends hea ily on he con igu a ion se up and also
o he con ol mechanism. Also measu ing he numbe o
sea ched windows pe o med by he de ec ion sys em gi es
mo e ealis ic in o ma ion abou he de ec ion pe o mance.
The speeds ob ained by he accele a ed Viola-Jones
implemen a ion in some con igu a ion a e as e ha any single
GPU accele a ion [3], [7] and o he highes numbe o sea ch
windows (Table III) i has close pe o mances o [3] and [7].
VII. HARDWARE ACCELERATION OF THE EMBEDDED FACE
DETECTION SYSTEM
A e a ca e ul analysis o he ace de ec ion applica ion i
was ound ha he so wa e bo leneck esided in he huge
amoun o memo y ead access, mul iplica ion, and squa ed
oo ope a ions done by all he sea ch window e alua ions. In
o de o de ec a ace om an image, hund eds o housands o
sea ch windows a e e alua ed and his ep esen s he mos ime
consuming pa o he applica ion. The e o e i was decided o
accele a e he e alua ion o sea ch windows by employing a
ha wa e IP module [14]. The p oposed IP module and all
in e nal componen s a e clocked by he sys em clock (80 Mhz).
In o de o keep a high deg ee o lexibly and sha e he
ha dwa e esou ces wi h he es o he LEON3 sys em i was
decided o he IMSE_OBJECT_DETECTION IP o ha e wo
ope a ing modes: he ee mode in which LEON3 p ocesso
can use he IP ha dwa e esou ces o implemen o he
unc ionali ies, and he ace de ec ion mode. Figu e 3 shows
he block diag am o he IP module.
Fig. 3. IMSE_OBJECT_DETECTION IP block diag am
As i was p e iously men ioned he IP module implemen s
he sea ch window algo i hm. The so wa e applica ion will
load he comp essed o m o Haa -like ea u es in o he Sha ed
Memo y be o e any de ec ion ope a ion. Be o e s a ing any
de ec ion ope a ion he con igu a ion egis e s (scale, x-y
coo dina es, image dimension, e c.) mus be con igu ed wi h
he desi ed con igu a ion alues. When he s a command is
gi en he ace de ec ion p ocedu e is ully con olled by he
componen Imse_s age_e alua o _uni (see Figu e 3). This uni
is he co e engine o accele a ing ace de ec ion. A he end o
he de ec ion he componen signalize i a ace is p esen , he
S a us egis e is upda ed wi h he de ec ion esul and an
in e up is gene a ed.
The e alua o uni has a mul iple s a e machine con ol in
o de o deal wi h a iable memo y access la encies. Beside he
mul iple s a e machine con ol his uni con ains specialized
modules. Haa _ ea u e_ ec _calc module is used o calcula e
he a ea o in eg al ec angles using only he co ne s da a.
Haa _ ea u e_scale is a pipelined module o Haa -like
ea u e scaling and sea ch window add ess compu a ion.
Sq 64_a ay_pipe16 is 64 bi pipelined in ege squa e oo
uni ha has da a ou pu la ency o 16 clocks. Mul41x33signed
is a 41x33 signed mul iplie . The Regis e Bank con ains APB
bus accessible egis e s ha a e used o he co e con igu a ion
and con ol. The APB Sla e In e ace connec s he IP module
o he APB bus and enables he LEON3 p ocesso o access he
egis e s om Regis e Bank. The AHB Mas e /Sla e In e ace
is a simple DMA in e ace.
The IP module has a sha ed memo y based on a dual-po
RAM wi h AHB in e ace. The Sha ed Memo y is used by he
IP module o s o e he comp essed Haa -like ea u es. When
he module wo ks in “F ee Mode” LEON3 can use he Sha ed
Memo y as addi ional on chip RAM memo y.
VIII. RESULTS
The p oposed LEON3 ace de ec ion sys em wo ks wi h
images (colo ed o g ey) ha ing a esolu ion smalle han
1024x1024 pixels. I ully uses he OpenCV cascade o
classi ie s o on al aces and i can s o e in o he IP Sha ed
Memo y app oxima ely 2730 Haa -like classi ie s. I also
wo ks wi h a g ea e numbe o Haa -like classi ie s bu he
ex a classi ie s mus be s o e in o he p og am memo y and
hen loaded in o he Sha ed Memo y a he app op ia e
momen .
The sys em was implemen ed on a Xilinx XC5VLX50
FPGA. The en i e LEON3 ace de ec ion sys em uses 6,435
slices (up o 89% o he de ice u iliza ion) and 10,962 o lip-
lops (up o 38% o he de ice u iliza ion). The es ima ed s a ic
powe consump ion (measu ed wi h Xpowe Analyze om
Xilinx) o he LEON3 co e is 603 mW. The mos powe
consuming componen s a e he DDR2 memo y con olle (216
mW), DVI in e ace (136.06 mW) and he clock gene a o s.
The LEON3 p ocesso consumes 32.39 mW and e en hough
he IMSE_OBJECT_DETECTION IP uses mo e lip lops and
has app oxima ely he same amoun o logic, i s powe
consump ion is 6 imes less (5.39 mW) han he LEON3
p ocesso .
A. Pe o mance
In o de o measu e he de ec ion pe o mances o he
LEON3 embedded de ec ion sys em h ee dis inc so wa e
implemen a ions o ace de ec ion we e compa ed:
 Po ed OpenCV so wa e o embedded sys ems.
 So wa e accele a ed e sion o he po ed so wa e.
 Ha dwa e + So wa e accele a ed e sion o he po ed
so wa e.
The measu ed pe o mances me ics a e he execu ion ime
and he numbe o sea ched windows pe o med. Fo he i s
wo implemen a ions he pe o mances we e measu ed o wo
dis inc modes o de ec ion (mode 1 and mode 2). Fo each
mode, ou di e en se -up pa ame e s we e used (se up 1 o 4)
o he minimum size sea ch window (S) and he scale s ep
(s ep): 1) S=30x30, s ep=1.2; 2) S=30x30, s ep=1.1; 3)
S=20x20, s ep=1.2; 4) S=20x20, s ep=1.1.

F om Figu e 4 i esul s ha he accele a ed ace de ec ion
applica ion is 3-4 imes as e han he po ed OpenCV
applica ion o bo h modes. Using he ha dwa e accele a ion IP
he ace de ec ion is 10-12 imes as e han he po ed ace
de ec ion applica ion unning exclusi ely on LEON3 p ocesso
co e.
Fig. 4. De ec ion imes o h ee dis inc implemen a ions o VGA image. a)
Scale Image mode (Mode 1), b) Scale Haa -like ea u es mode (Mode 2)
B. De ec ion Accu acy
In o de o analyze he de ec ion accu acy an speci ic
so wa e has been de eloped. The PC based es bench
so wa e con igu es LEON3 based de ec ion sys em and send
he es images. Then i ecei es he de ec ion esul s o u he
analysis. The es se up is based on 2409 on al ace images
om he colo FERET da abase [15].
The accu acy o ace de ec ion can be desc ibed using
ecei e ope a ing cha ac e is ic (ROC), which is a cu e
widely adop ed in signal-de ec ion heo y. An ROC is
essen ially a sca e plo ha shows he ela ionship be ween he
alse accep ance a e and he ue accep ance a e. Because he
numbe o e alua ed nega i e egions is e y high o de ec ion
algo i hms, o analysis, i was adop ed a modi ied e sion o
ROC cu e in oduced in FDDB[16] in which he ho izon al
axis con ains only alse posi i e o he en i e es image se .
Figu e 5 shows he ROC cu es o OpenCV so wa e and
he IP module. Bo h ha e e y simila esul s. The e is a small
di e ence be ween he wo ROC cu es because he da a esul
(i.e de ec ed aces) agg ega ion mechanism is sligh ly di e en
in he analysis ools.
(a) (b)
Fig. 5. ROC cu es: a) OpenCV ace de ec ion so wa e,
b) IMSE_OBJECT_DETECTION IP based sys em
IX. CONCLUSIONS
This communica ion p esen s a design me hodology o
ace de ec ion embedded implemen a ion. The s a ing poin
was OpenCV lib a y esou ces o ideo ace de ec ion. Fo i
some modi ica ions has been make adap ing and op imizing he
OpenCV baseline applica ion o an embedded en i onmen .
This modi ica ions can be summa ized in changing he loa ing
poin ope a ions by in ege ones, analyzing he pe o mance in
o de o de ec he sys em bo lenecks, and algo i hmic speed-
up by no scaling he Haa -like ea u e weigh s and adjus ing
he compu a ion o a iance. Finally, hose asks equi ing a
g ea e compu a ion a e implemen ed in ha dwa e, accele a ing
he ace de ec ion p ocess, and enabling i s applica ion in
embedded sys ems ha equi e eal ime.
ACKNOWLEDGEMENT
This wo k was suppo ed in pa by Spanish Minis e io de
Ciencia y Tecnología unde he P ojec TEC2011-24319, and
by Jun a de Andalucía unde he P ojec P08-TIC-03674. Co-
inanced by FEDER.
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