Ci a ion: Mel sen, A.; Lepsien, A.;
Bosselmann, J.; Koschmide , A.;
Ha ung, E. Desc ibing Beha io
Sequences o Fa ening Pigs Using
P ocess Mining on Video Da a and
Au oma ed Pig Beha io Recogni ion.
Ag icul u e 2023,13, 1639.
h ps://doi.o g/10.3390/
ag icul u e13081639
Academic Edi o s: Imke T aulsen and
Mehme Gül as
Recei ed: 15 July 2023
Re ised: 9 Augus 2023
Accep ed: 16 Augus 2023
Published: 21 Augus 2023
Copy igh : © 2023 by he au ho s.
Licensee MDPI, Basel, Swi ze land.
This a icle is an open access a icle
dis ibu ed unde he e ms and
condi ions o he C ea i e Commons
A ibu ion (CC BY) license (h ps://
c ea i ecommons.o g/licenses/by/
4.0/).
ag icul u e
A icle
Desc ibing Beha io Sequences o Fa ening Pigs Using P ocess
Mining on Video Da a and Au oma ed Pig Beha io Recogni ion
And eas Mel sen 1,* , A id Lepsien 2, Jan Bosselmann 2, Agnes Koschmide 3,4 and Ebe ha d Ha ung 1
1Ins i u e o Ag icul u al Enginee ing, Facul y o Ag icul u al and Nu i ional Sciences, Kiel Uni e si y,
24118 Kiel, Ge many; eha ung@il .uni-kiel.de
2Depa men o Compu e Science, Facul y o Enginee ing, Kiel Uni e si y, 24118 Kiel, Ge many;
[email p o ec ed] (A.L.); [email p o ec ed] (J.B.)
3
Business & In o ma ion Sys ems Enginee ing, Facul y o Law, Business and Economics, Uni e si y o Bay eu h,
95447 Bay eu h, Ge many; agnes.koschmide @uni-bay eu h.de
4F aunho e FIT, 95444 Bay eu h, Ge many
*Co espondence: amel sen@il .uni-kiel.de
Abs ac :
This s udy aimed o demons a e he applica ion o p ocess mining on ideo da a o pigs,
acili a ing he analysis o beha io al pa e ns. Video da a we e collec ed o e a pe iod o 5 days
om a pig pen in a mechanically en ila ed ba n and used o analysis. The app oach in his s udy
elies on a se ies o indi idual s eps o allow p ocess mining on his da a se . These s eps include
objec de ec ion and acking, spa io empo al ac i i y ecogni ion in ideo da a, and p ocess model
analysis. Each s ep gi es insigh s in o pig beha io a di e en ime poin s and loca ions wi hin
he pen, o e ing inc easing le els o de ail o desc ibe ypical pig beha io up o p ocess models
e lec ing di e en beha io sequences o clus e ed da ase s. Ou da a-d i en app oach p o es
sui able o he comp ehensi e analysis o beha io al sequences in con en ional pig a ming.
Keywo ds: beha io sequences; p ocess mining; AI ideo analysis; a ening pigs; unc ional a eas
1. In oduc ion
P ocess mining is a well-es ablished me hod o gaining insigh in o da a by s uc u ing
i in o a sequence o ac i i ies, known as a p ocess model [
1
]. The me hod has been
success ully applied o a ious domains, including heal hca e, inance, and manu ac u ing
and is mainly used o iden i y bo lenecks o compliance issues wi hin p ocesses. Al hough
p ocess mining has been p ima ily used in he business con ex , i can also p o ide bene i s
o disciplines dealing wi h high olumes and e aci y o da a, such as li e o na u al
science. These disciplines, howe e , o en equi e a s uc u ed app oach o answe ing
p ocess- ela ed ques ions, like in ou scena io, o iden i y beha io pa e ns o g oups
o animals.
Obse ing al e a ions in he beha io p ocesses o pigs can be a help ul ool o ana-
lyzing and e alua ing animal beha io , animal heal h and en i onmen al impac . Howe e ,
mos app oaches on iden i ying pig beha io based on ideo da a so a mos ly ocus on
single speci ic ac i i ies e.g., eeding/d inking ecogni ion, ail bi ing, playing beha io , o
agg ession ecogni ion [2].
Di e en lying pa e ns o pigs can be iden i ied wi h compu e ision-based moni-
o ing o gi e an indica ion o animal heal h, wel a e and he mal com o s a e o g oup-
housed pigs indica ing clima e condi ions in mechanically en ila ed ba ns. Likewise
obse a ions o ac i i y and eed in ake, which a y depending on di e en clima e condi-
ions, suppo he con ol o he abo e [3].
Analyzing he eeding beha io o pigs suppo s he indica ion o heal h issues and
can play an impo an ole in he b eeding p ocess. Me hods o iden i ying eeding beha io
based on ideo da a ha e been published wi h a good success a e [4].
Ag icul u e 2023,13, 1639. h ps://doi.o g/10.3390/ag icul u e13081639 h ps://www.mdpi.com/jou nal/ag icul u e
Ag icul u e 2023,13, 1639 2 o 20
Beha io changes, like slowdown and weakening, can be used as an ea ly wa ning indi-
ca o o pa hological in ec ions. De ec ing hese beha io al changes as pa o su eillance in
eal- ime suppo s a ms in hei moni o ing asks [
5
]. S udies o
Be gamini e al., 2021 [6]
ha e shown ha gene al beha io al changes can also be ex ac ed om la ge compu e -
ision da ase s and ma k long- e m changes in i e undamen al indi idual beha io
pa e ns. Those e ec s on gene al beha io such as lying, mo ing, eeding, d inking and
elimina ing, a e also ele an in e ms o , en i onmen al impac . Pigs a e known o ha e he
abili y o s uc u e hei pens in o unc ional a eas o elimina ion, eeding, and sleeping i
ce ain condi ions in e ms o , e.g., space allowance a e me . This beha io is in luenced by
hei inna e need o hygiene and com o , as well as hei social hie a chies and pa e ns o
use. S udies ha e shown ha pigs end o es ablish clea bounda ies be ween elimina ion
and sleeping a eas, wi h he o me being loca ed a a dis ance om he la e [
7
]. Feed-
ing a eas, on he o he hand, a e o en loca ed nea bo h elimina ion and sleeping a eas,
e lec ing hei impo ance in he daily ou ine o pigs [
8
]. Sel -s uc u ed pig pens consis
o a de ined soiling a ea in which no mal elimina o y beha io akes place [
7
]. In case o
de ia ing elimina ion beha io due o changes in pig densi y, pa i ion ype o changes
in clima e condi ions [
8
,
9
] po en ially inc eased ammonia emissions ha will nega i ely
in luence he en i onmen al impac ela ed o he size o he soiled a ea co esponds o he
eleased ammonia emissions [10].
Also, he ea ly de ec ion o heal h and wel a e comp omises such as he iden i ica ion
o clinical and subclinical illnesses can be obse ed based on ecognized gene al beha io al
changes in pigs wi h esul s om ideo da a [
11
,
12
]. Simila epo s exis o he analysis
o social in e ac ions such as agonis ic beha io a e eg ouping as an ea ly indica o o
misma ches wi hin he g oup [13–15].
This pape p oposes o use p ocess mining as a me hod o iden i y beha io al pa e ns
o a ening pigs and he ela ed p ocesses om ideo da a. The goal is o demons a e
ha p ocess mining can be a aluable ool o unde s anding he beha io o pigs in hei
beha io sequences and gaining addi ional in o ma ion in a empo al, spa ial esolu ion
abou he di ision o he pen in unc ional a eas compa ed o he s a e-o - he a me hod.
This publica ion explains he s ep-by-s ep app oach leading o he e alua ion h ough he
me hod o p ocess mining. I sys ema ically demons a es he p ocedu e by which he
analysis un olds. The indi idual s ages include: (1) iden i ica ion o p ocess ac i i ies,
(2) iden i ica ion o dis inc beha io al pa e ns and assessmen o hei accu acy, (3) depic-
ion o beha io al pa e ns in empo al and spa ial esolu ion, and (4) ans o ma ion o
beha io al pa e ns in o p ocess models.
The me hodology in his pape is gene ally based on he app oach p esen ed in [
16
]
and ex ends i in e ms o adap ing he me hodology o a eal-li e use case, an imp o ed
e alua ion, and an in-dep h discussion o he analysis esul s. In [
16
], he iden ical ideo
da ase was used o e alua e he p oposed me hods, wi h b ie analysis esul s ela ed o
hei capabili y o p oduce meaning ul ou comes. This pape demons a es he adap ed
app oach o analyzing ideo eco dings o a ening pigs in subsequen s eps o he da a-
d i en app oach o ex ac aluable in o ma ion abou hei o e all beha io and beha io
sequences. The da a-d i en app oach’s esul s can aid in imp o ing decision-making
ega ding pig beha io sequences.
2. Ma e ials and Me hods
2.1. Animals and Housing
Video eco dings o a ening pigs we e eco ded a he Teaching and Resea ch Facili y
Fu e kamp o he Schleswig-Hols ein Chambe o Ag icul u e. In o al, 1400 Pigs we e
housed in 14 compa men s in he expe imen al ba n on si e. Each compa men was
mechanically en ila ed and subdi ided in o en pens ( i e pe side—one behind ano he ,
di ided by an inspec ion walkway) each 4.08 m wide
×
2.74 m long, each consis ing o
11 pigs. Thus, each pig had 1.02 m2o space a i s disposal.
Ag icul u e 2023,13, 1639 3 o 20
All a ening pigs we e ea ed in con en ional pig pens wi h mechanical en ila ion.
The space allowance in his in es iga ion was clea ly abo e he minimum se by he
Eu opean o Ge man legisla ion based on li e weigh [17,18]. Insu icien space can cause
nega i e social beha io owa d penma es, esul ing in skin lesions, lameness, ail bi ing, o
educed g ow h [
19
]. In addi ion o he space allowance he beha io o a ening pigs can
be in luenced by a ious ac o s, such as diu nal hy hms, eeding, social in e ac ions, and
en i onmen al condi ions. All condi ions we e kep unchanged du ing he es ing pe iod
o minimize ex e nal in e e ences.
The ba n clima e in he s udy pe iod was no in luenced by he s udy, bu ollowed
he pa ame e s o he s udy ba n used in p ac ice.
The whi e luo escen ube ligh s we e swi ched on du ing he day ime (06:00–16:00
o’clock). All pens we e equipped wi h ully-sla ed conc e e loo s and an unde - loo
slu y pi . Pigs we e ed up o se en imes pe day ( eeding imes: 07:33, 09:25, 10:50, 12:31,
14:52, 16:52 on all days and addi ionally a 17:44 on he i s wo days) wi h a p ede ined
amoun o liquid eed in a long ough sys em, gi ing enough eeding space o all pigs
o simul aneously ha e access o he ough. In gene al, he eeding a io was adjus ed in
h ee phases acco ding o he weigh o he animals (ea ly 25 kg, middle 40 kg, la e 70 kg).
Du ing he in es iga ions all pigs we e ed wi h he i s eed. As en ichmen ma e ial sisal
opes we e a ached o one wall o he pen.
A ske ch o he pen design wi h de ails like posi ion o eeding ough as well as he
posi ion o he came as wi hin he compa men can be seen in Figu e 1.
Ag icul u e 2023, 13, x 3 o 21
mechanically en ila ed and subdi ided in o en pens ( i e pe side—one behind ano he ,
di ided by an inspec ion walkway) each 4.08 m wide × 2.74 m long, each consis ing o 11
pigs. Thus, each pig had 1.02 m² o space a i s disposal.
All a ening pigs we e ea ed in con en ional pig pens wi h mechanical en ila ion.
The space allowance in his in es iga ion was clea ly abo e he minimum se by he Eu-
opean o Ge man legisla ion based on li e weigh [17,18]. Insufficien space can cause
nega i e social beha io owa d penma es, esul ing in skin lesions, lameness, ail bi ing,
o educed g ow h [19]. In addi ion o he space allowance he beha io o a ening pigs
can be in luenced by a ious ac o s, such as diu nal hy hms, eeding, social in e ac ions,
and en i onmen al condi ions. All condi ions we e kep unchanged du ing he es ing pe-
iod o minimize ex e nal in e e ences.
The ba n clima e in he s udy pe iod was no in luenced by he s udy, bu ollowed
he pa ame e s o he s udy ba n used in p ac ice.
The whi e luo escen ube ligh s we e swi ched on du ing he day ime (06:00–16:00
o’clock). All pens we e equipped wi h ully-sla ed conc e e loo s and an unde - loo
slu y pi . Pigs we e ed up o se en imes pe day ( eeding imes: 07:33, 09:25, 10:50, 12:31,
14:52, 16:52 on all days and addi ionally a 17:44 on he i s wo days) wi h a p ede ined
amoun o liquid eed in a long ough sys em, gi ing enough eeding space o all pigs o
simul aneously ha e access o he ough. In gene al, he eeding a io was adjus ed in
h ee phases acco ding o he weigh o he animals (ea ly 25 kg, middle 40 kg, la e 70 kg).
Du ing he in es iga ions all pigs we e ed wi h he i s eed. As en ichmen ma e ial sisal
opes we e a ached o one wall o he pen.
A ske ch o he pen design wi h de ails like posi ion o eeding ough as well as he
posi ion o he came as wi hin he compa men can be seen in Figu e 1.
Figu e 1. Floo plan o compa men 6 wi h he came a (A) acing pen 68 which was
used o ideo eco ding. (B) Posi ion o he eeding ough.
The 11 boa s o Topigs, PIC and DK gene ic in pen #68 we e on a e age 107.6 d (SD
4.3 d) and weighed 56.06 kg (SD 5.25 kg). All pigs we e andomly dis ibu ed e enly
among he compa men s acco ding o gene ics and weigh .
Figu e 1.
Floo plan o compa men 6 wi h he came a (A) acing pen 68 which was used o ideo
eco ding. (B) Posi ion o he eeding ough.
The 11 boa s o Topigs, PIC and DK gene ic in pen #68 we e on a e age 107.6 d
(SD 4.3 d) and weighed 56.06 kg (SD 5.25 kg). All pigs we e andomly dis ibu ed e enly
among he compa men s acco ding o gene ics and weigh .
2.2. Video Reco dings
To moni o he beha io o pigs, a ideo eco ding se up was used consis ing o
a came a and an HDD ecei e uni ANNKE H500 (ANNKE Secu i y Technology Inc.,
Rowland Heigh s, CA, USA). The came a was posi ioned acing he pigs This placemen
Ag icul u e 2023,13, 1639 4 o 20
allowed o a comp ehensi e iew o almos he en i e pigpen and cap u ed he mo emen s
o he pigs. Due o he low ceiling le el he came as we e posi ioned a a heigh o
app oxima ely 2.9 m abo e he loo and angled downwa d a 45
◦
o ensu e ha almos he
en i e pen was cap u ed in he ield o iew. The came as we e se o a esolu ion o 1080 p
and a ame a e o 12.5 ames pe second. This se ing allowed o clea and de ailed
eco dings o he pigs’ beha io . The came as we e also se o in a ed mode o cap u e he
eco dings in low-ligh condi ions which was he case as soon as he ligh s we e u ned o .
All ideo eco dings we e s o ed in he HDD ecei e uni in a o ma ha was compa ible
wi h he so wa e used o da a analysis.
The ideo eco dings we e aken in he ime pe iod om 12 No embe 2021 o
10 Decembe 2021 be ween 06:00 am and 06:00 pm. This ime pe iod was chosen because
i co e ed he majo i y o he pigs’ ac i e hou s as ac i i y le el signi ican ly dec eases
du ing nigh hou s [
20
]. The eco dings we e aken con inuously wi hou in e up ion o
ensu e ha no beha io was missed.
2.3. P ep ocessing o Video Da a
Fi s , he ideo eco dings we e p epa ed by (1) selec ing he ideo segmen s ele an
o he analysis being conduc ed and (2) o ganizing he selec ed ideo segmen s in o a
s uc u ed da ase acco ding o a uni o m o ma . Since he pu pose o he analysis does
no impose any es ic ions on he ime o day o loca ion o he beha io s o be moni o ed,
all eco dings we e in p inciple ele an . Howe e , inal eco dings om i e consecu i e
days (13 No embe 2021–17 No embe 2021) we e selec ed and all o he eco dings we e
excluded om he analysis, as p ocessing esou ces we e limi ed and hese selec ed days
we e su icien o cap u e ep esen a i e beha io s.
The selec ed ideo eco dings, which we e spli in o mul iple ideo iles pe day due
o he eco ding se up, we e consolida ed in o a single ile pe selec ed day. To educe
p ocessing ime, he 1080 px esolu ion o he ideo da a was educed o 854
×
480 px.
While downscaling ideo da a signi ican ly educes p ocessing ime o ideo analysis,
excessi e downscaling can esul in loss o in o ma ion (e.g., due o small objec s becoming
blu ed in he images). Fo he analysis in his s udy, small de ails we e negligible, because
he analysis o b oad beha io al classes did no equi e ine-g ained de ails o dis inguish
di e en a ian s o he same class, and he e o e he downscaling o 854
×
480 px p omised
o be an app op ia e solu ion. Since he models used la e o ac i i y ecogni ion equi ed
a ame a e o 30 ps as inpu , ideos we e esampled o ha ame a e using FFmpeg
(FFmpeg p ojec ). The eco dings also cap u ed some beha io in neighbo ing pig pens. To
educe noise in p ocessing and he analysis, a s a ic mask was o e laid o e he ideo iles,
so ha only he a ge ed pig pen was isible.
To quan i y he amoun o ac i i y isible in a ideo sequence wi hou ha ing o
ac ually de ec he mo emen and execu e ac i i ies o he pigs, a cus om ac i i y sco e
based on pixel changes be ween ames we e used [
21
]. To calcula e he ac i i y sco e
du ing a gi en ideo, ames a e sampled a a ixed a e and compa ed o he p e iously
sampled ame. Each ame was di ided in o a 20
×
20 g id and he mean alues o
each g id we e calcula ed a a ixed sampling a e o one ame e e y 30 s. Due o he
di e en ligh ing condi ions du ing he day, each ame was con e ed o a g ayscale
image be o e pe o ming he calcula ions. The ac i i y sco e was hen calcula ed as he
sum o he absolu e di e ences o he pixel alues o each co esponding g id be ween
wo sampled ames. The alues we e smoo hed using a mo ing a e age wi h a window
size o 60 samples, which means ha he inal ac i i y sco e a a gi en poin in ime akes
in o accoun alues om i s pas and u u e 15 min. Depending on he goal o he analysis,
he ac i i y sco e could be used o il e ou i ele an segmen s o he ideo eco dings
(e.g., segmen s whe e gene ally a low amoun o ac i i y is obse ed).
Ag icul u e 2023,13, 1639 5 o 20
2.4. De ec ion and T acking
The nex s ep o he analysis in ol ed de ec ing he posi ions o pigs in he ideo and
acking pigs h oughou he ideo eco dings. Fo pig de ec ion, a da ase o 614 anno a ed
images was c ea ed by andomly sampling ames om he ull se o ideo eco dings and
manually labeling he pigs isible in hese ames wi h bounding boxes.
This da a se was andomly spli in o aining and alida ion se s a an 80:20 spli .
The aining da a se was used o ine une a YOLO 7 classi ie [
22
], which was p e- ained
on ImageNe [
23
]. By using he pa ame e s o a ne wo k p e ained on ImageNe and
ine- uning o he cus om ask (in ou case he pig de ec ion), he amoun o equi ed
aining images was signi ican ly educed compa ed o aining om sc a ch [24].
Fo mul iple objec acking, a By eT ack acke [
25
] was used. By eT ack is a acking-
by-de ec ion me hod, i.e., i uses de ec ions p o ided by a sepa a e objec de ec o and
co ela es he de ec ions ac oss ames h ough in e nal mo ion models. This p o ides
he lexibili y o easily adap he acke o new se ings and use cases, which can be
implemen ed by simply adap ing he objec de ec o o he new se ing (e.g., a di e en
came a pe spec i e, a chi ec u e, o species) [26].
2.5. Ac i i y Recogni ion (Labeling o Animal Beha io )
The nex s ep a e localizing and acking he pigs is he de ec ion o hei ac i i ies.
To suppo he ans e abili y o ou app oach o o he se ings and analysis goals, a
gene ic ac i i y ecogni ion was equi ed allowing o de ec mul iple concu en beha io s
occu ing in a ideo. Mos exis ing me hods, howe e , o pig beha io ecogni ion ei he
allow he de ec ion o single ac i i ies o speci ic g oups o ac i i ies [
2
]. O hose solu ions
ha a e able o de ec mul iple di e en ac i i y classes, mos s ill use ac i i y-speci ic
assump ions and algo i hms (e.g., a pig wi h i s head posi ioned a a known posi ion o
a d inke is classi ied as d inking in [
6
]). To educe he need o explici ly impose any
assump ions in o he ac i i y ecogni ion, we selec ed a deep lea ning me hod based on
CNNs (Con olu ional Neu al Ne wo ks) ha lea ns o de ec ac i i ies based on labeled
aining examples o ac i i ies occu ing in a ideo. In pa icula , SlowFas [
27
] was used
o spa io- empo al ac ion de ec ion, which is a ask om compu e ision conce ned wi h
de ec ing he ac i i ies concu en ly pe o med by mul iple objec s in a ideo.
Li e al., 2020 [
28
] showed ha spa io- empo al CNNs can success ully be applied
o pig beha io ecogni ion. They speci ically op imized he design o a SlowFas -based
CNN o pig beha io ecogni ion. In hei e alua ion, his design signi ican ly imp o ed
accu acy o unseen se ings (e.g., a came a pe spec i e di e en om hose obse ed
in he aining se ), bu he accu acy was compa able o mo e gene ic ne wo k designs
(i he se ing is simila in aining and in e ence). Gene ally, ne wo ks p e- ained on
Kine ics [
29
], which is a ideo da ase o ac i i ies pe o med by humans, pe o med be e
han he non-p e- ained ne wo ks, wi h he excep ion o he ne wo k specially designed
o pig beha io ecogni ion.
As o he analysis in his s udy, he se ing o he aining and analysis ideos was
iden ical, we chose o use a gene ic SlowFas 4
×
16 ne wo k p e- ained on Kine ics. A
cus om aining da ase was p epa ed by sampling sho ideo sequences om he ull
se o ideo eco dings, con aining bo h RGB and in a ed segmen s as well as segmen s
wi h high and low gene al ac i i y. The beha io obse ed in hese aining sequences
was manually anno a ed by a ained obse e acco ding o he beha io classes de ined
in Table 1, esul ing in a o al o 9240 anno a ed samples o ac i i ies. This da ase was
di ided in o 70:30 aining/ alida ion spli s, and used o ine- une he p e- ained SlowFas
4×16
model. P e- aining using he da ase p o ided by Be gamini e al., 2021 [
6
] was
also e alua ed, bu did no yield any signi ican imp o emen s in aining con e gence
speed o quali y.

Ag icul u e 2023,13, 1639 6 o 20
Table 1. Beha io s o pigs and he espec i e de ini ions as used in he da ase .
Beha io De ini ion
lying Pig in a es ing posi ion, ypically lying down on he side o he unk o
ches /s e num, wi h minimal mo emen and i s body suppo ed by he loo .
si ing
Con ac o he loo wi h he ee o he on legs and he pos e io po ion o
he pig’s body.
s anding Con ac o he loo wi h all ee wi hou changing posi ion.
mo ing Pig in mo ion, displaying walking o unning beha io , wi h all ou legs
ac i ely mo ing hei body o wa d.
in es iga ing
Pig using i s snou o sea ch and dig in o he g ound o o he su aces, o en
in a epe i i e manne , as pa o i s na u al beha io o ind ood o explo e
he su oundings.
eeding
Focused engagemen wi h a ood sou ce, cha ac e ized by epea ed chewing,
swallowing, and oo ing beha io .
de eca ing
Ac o elimina ion, seen as a s anding posi ion, ollowed by he expulsion o
eces om he body.
playing Ac i e engagemen wi h occupa ion ma e ial o pigs like opes.
miscellaneous All o he ac i i ies ha canno be alloca ed o any o he abo e ac i i ies.
The ained model was hen used o de ec he ac i i ies execu ed by he pigs in
he comple e i e days o ideo eco dings selec ed o analysis. The esul s o ac i i y
ecogni ion indica e he loca ion and ime o each de ec ed ac i i y, and can be used in
combina ion wi h he p e iously ex ac ed acking in o ma ion o econs uc sequences
o ac i i ies pe o med by speci ic pigs.
2.6. P ocess Mining
The inal pa o he analysis is he applica ion o p ocess mining o he ex ac ed
ac i i ies. P ocess mining algo i hms ypically equi e s uc u ed e en da a a a high le el
o abs ac ion, i.e., a log o e en s desc ibing when each ac o s a s and ends wi h he
execu ion o speci ic ac i i ies, which in u n e e s o speci ic ins ances o a p ocess (cases).
Albei al eady e e ing o he abs ac ac i i ies ele an o he conduc ed p ocess mining
analysis, he esul s om ac i i y ecogni ion a e a a much lowe le el o abs ac ion,
because hey simply lis he ac i i ies de ec ed o each pig a egula ime in e als, and
a e no o ganized in o cases. To ans o m he ac i i y ecogni ion esul s in o a s uc u e
complying wi h p ocess mining, he me hod p esen ed in [
16
] was used. Speci ically,
E en Abs ac ion and Case Co ela ion we e used o p epa e he inpu o he p ocess
mining algo i hms. Fo E en Abs ac ion, mul iple subsequen ly ollowed de ec ions o
he same ac i i y o he same pig a e agg ega ed o a single ins ance o his ac i i y using
he empo al agg ega ion echnique wi h smoo hing om [
16
]. In ypical p ocess mining
applica ions, whe e he unde lying p ocess is a well-s uc u ed business p ocess, cases can
ypically be de ined by he s a - and endpoin s o he p ocess execu ions (e.g., a business
p ocess could s a when an o de o a p oduc is ecei ed and inish when he p oduc
has been shipped). In he daily beha io o pigs, howe e , no such na u al no ion o a case
exis s. To cons uc cases in a way ha each case ep esen s an ins ance o he same p ocess,
Case Co ela ion is pe o med using de ined s a and end ac i i ies using he echnique
om [
16
]. Fo his, lying was de ined as bo h he s a and end ac i i y o each p ocess
ins ance. This means each ins ance o he analyzed p ocess s a s when a pig s ands up and
ends when his same pig lies back down, and he e o e cap u es “ac i e” phases o he pigs.
As he beha io o pigs is uns uc u ed and chao ic, ace clus e ing [
30
] is hen applied
o he e en da a. T ace clus e ing me hods di ide he e en da a o an uns uc u ed
p ocess in o mul iple, sepa a e clus e s e e ing o simila beha io . Wi h ace clus e ing,
p ocess mining me hods a e applied o each clus e sepa a ely, which ypically yields mo e
Ag icul u e 2023,13, 1639 7 o 20
s uc u ed esul s. Fo ou analysis, ea u es such as he occu ence and equency o e en s,
and he di ec ly- ollows ela ionships (i.e., i mo ing occu s di ec ly a e lying in a case,
mo ing di ec ly ollows lying) in a case we e ex ac ed, scaled by i s a ge ing a mean
a ound ze o and hen scaling o uni a iance, educed wi h PCA se o 99% a iance, and
o ganized in o 15 clus e s using he k-means algo i hm. Finally, he clus e ed e en da a
we e expo ed in o a ep esen a ion p ocessed by p ocess mining ools and p ocess models
we e disco e ed o each o he clus e s wi h he p ocess mining so wa e Disco (Fluxicon
BV, Eindho en, The Ne he lands).
3. Resul s
The esul s co e he quan i ica ion o ac i i ies in spa ial and empo al esolu ion
wi h a ocus on he de elopmen o a p ocess mining analysis o beha io ecogni ion in
pigs. Fo p ocess mining analysis, i s a pu pose needs o be de ined [
31
]. This pu pose
guides he analysis wi h espec o how he da a a e p epa ed, how e en s a e ex ac ed and
p ocessed, and which p ocess mining algo i hms a e applied o he ex ac ed e en da a.
Fo he analysis in his s udy, he daily beha io o pigs was moni o ed wi h he
pu pose o ex ac ing pa e ns desc ibing he beha io o pigs in ac i e phases, wi h he
goal o enabling au oma ic moni o ing o he ac i i y o pigs. The s epwise app oach
including gene al ac i i y quan i ica ion, iden i ica ion o dis inc beha io al pa e ns, and
inally ans o ming he beha io al pa e ns in o p ocess models, was pe o med o achie e
his goal. The esul s o hese s eps a e p esen ed in he ollowing chap e .
3.1. Gene al Ac i i y in he Pen—Quan i ica ion o Ac i i ies in Spa ial and Tempo al Resolu ion
Figu e 2shows a e age ac i i y sco es o he whole pen o e ime du ing eco ding
imes (06:00–18:00) o di e en obse a ion days. Clea ly ecognized ac i i y pa e ns
appea a speci ic imes du ing he day ha co espond wi h eeding imes and he gene al
ba n ou ine. Fixed ime pe iods can be iden i ied acco ding o an inc ease in ac i i y
occu ence, e.g., simila inc eases in ac i i y a all obse a ion days a e no ed a ound
07:30–08:15, 09:15–10:15, 10:45–11:30, 12:45–13:30, 14:45–15:30, 16:45–17:15 and addi ionally
a 17:45 un il obse a ion end. Fu he mo e, a link can be se o he eeding which akes
place oge he wi h he con ol walk be ween 07:10 and 07:30.
Ag icul u e 2023, 13, x 8 o 21
Figu e 2. Ac i i y sco es o e ime du ing eco ding imes (06:00–18:00) o i e consecu i e obse -
a ion days (13 No embe 2021–17 No embe 2021).
Ac i i y pa e ns du ing he day may also be in luenced by a i icial ligh ing in he
ba n. Ligh ing was ac i a ed om 06:00 un il he las con ol walk (be ween 15:00 and
16:00). Ou side o hese imes, an o ien a ion ligh was used o he animals. In addi ion
o he mo e seman ically abs ac ac i i ies, uni o m pe iods o es we e obse ed be-
ween he i s obse a ion isi o he s aff and he i s eeding and be ween he i s wo
eeding e en s espec i ely, du ing which he ac i i y sco e was signi ican ly educed on
all days. In con as , ac i i ies a e he hi d and ou h eeding phases showed a highly
he e ogeneous dis ibu ion. Howe e , no signi ican conclusion can be made abou he
speci ic ac i i ies (e.g., eeding, lying, de eca ing, e c.) o whe e hese ac i i ies ook place.
Figu e 3 illus a es he spa ial dis ibu ion o ela i e ac i i y sco es wi hin he pen a
diffe en ime poin s o one speci ic day (13 No embe 2021). Diffe en ac i i y pa e ns
can be obse ed be o e he i s eeding phase in he mo ning be ween 06:15–06:45, du ing
he second eeding phase be ween 09:15–09:45 and du ing a he e ogeneous ac i i y phase
be ween 15:30–16:00.
The spa ial dis ibu ion o he ela i e ac i i y sco e shows clea diffe ences o ac i -
i y oci wi hin he bay a diffe en imes o he day, wi h a e y low numbe o ac i i ies
in he ea ly mo ning hou s be o e he i s eeding and a highe numbe o ac i i ies a
eeding imes, as well as in he la e a e noon hou s be ween indi idual eeding pe iods.
Figu e 2.
Ac i i y sco es o e ime du ing eco ding imes (06:00–18:00) o i e consecu i e obse a-
ion days (13 No embe 2021–17 No embe 2021).
Ag icul u e 2023,13, 1639 8 o 20
Ac i i y pa e ns du ing he day may also be in luenced by a i icial ligh ing in he
ba n. Ligh ing was ac i a ed om 06:00 un il he las con ol walk (be ween 15:00 and
16:00). Ou side o hese imes, an o ien a ion ligh was used o he animals. In addi ion o
he mo e seman ically abs ac ac i i ies, uni o m pe iods o es we e obse ed be ween
he i s obse a ion isi o he s a and he i s eeding and be ween he i s wo eeding
e en s espec i ely, du ing which he ac i i y sco e was signi ican ly educed on all days. In
con as , ac i i ies a e he hi d and ou h eeding phases showed a highly he e ogeneous
dis ibu ion. Howe e , no signi ican conclusion can be made abou he speci ic ac i i ies
(e.g., eeding, lying, de eca ing, e c.) o whe e hese ac i i ies ook place.
Figu e 3illus a es he spa ial dis ibu ion o ela i e ac i i y sco es wi hin he pen a
di e en ime poin s o one speci ic day (13 No embe 2021). Di e en ac i i y pa e ns
can be obse ed be o e he i s eeding phase in he mo ning be ween 06:15–06:45, du ing
he second eeding phase be ween 09:15–09:45 and du ing a he e ogeneous ac i i y phase
be ween 15:30–16:00.
Ag icul u e 2023, 13, x 9 o 21
Figu e 3. Spa ial dis ibu ion o ela i e ac i i y sco es wi hin he pen a diffe en ime poin s on one
speci ic day (13 No embe 2021).
Du ing he second eeding phase, ac i i y inc eased in he a ea o he eeding ough,
which indica es i s link wi h eeding beha io bu also in he lying a ea o he pen. He e -
ogeneous dis ibu ions o ac i i y (ac i i y sco e), dis ibu ed h oughou he pen wi h no
ocal poin o ac i i y, can be obse ed in he hea map (shown in Figu e 3) om he pe iod
o he la e a e noon, which also shows highe ac i i y in he ecal a ea in he op igh
co ne . Howe e , he ac i i y sco e is no sufficien o conclude wha ype o beha io is
being unde aken in each a ea o he pen.
3.2. Accu acy Objec , T acking and Beha io Recogni ion
The objec de ec o was e alua ed using he alida ion spli o he pig de ec ion da-
ase , esul ing in a [email protected]:0.95 o 0.864. In he selec ed ideos, on a e age 10.6 o 11
pigs (wi h a s anda d de ia ion o 0.64) we e de ec ed pe ame. The main challenge o
he objec de ec o was isual occlusion, i.e., when a pig was hidden om he iew o he
came a by o he pigs’ bodies. When mo e han 11 pigs we e de ec ed in a ame, he
bounding boxes wi h he lowes de ec ion con idence we e disca ded such ha 11 de ec-
ions emained.
To e alua e objec acking pe o mance, an e alua ion da ase o 14 1-minu e ideo
sequences was sampled om he eco dings and manually anno a ed wi h g ound- u h
acking in o ma ion. The acking pe o mance me ics calcula ed by compa ing he
acking esul s on hese ideos o he g ound- u h anno a ions a e epo ed in Table 2.
When applied o he selec ed ideos, he ackle s eached an a e age leng h o 18 min
be o e an ID swi ch o ackle agmen a ion, and he longes ackle s spanned o e mul-
iple hou s. While his is no op imal (which would p oduce one 12-h ackle pe pig pe
day), he a e age ackle leng h was sufficien o cap u e beha io al pa e ns consis ing
o mul iple, sequen ially execu ed ac i i ies.
Figu e 3.
Spa ial dis ibu ion o ela i e ac i i y sco es wi hin he pen a di e en ime poin s on
one speci ic day (13 No embe 2021).
The spa ial dis ibu ion o he ela i e ac i i y sco e shows clea di e ences o ac i i y
oci wi hin he bay a di e en imes o he day, wi h a e y low numbe o ac i i ies in he
ea ly mo ning hou s be o e he i s eeding and a highe numbe o ac i i ies a eeding
imes, as well as in he la e a e noon hou s be ween indi idual eeding pe iods.
Du ing he second eeding phase, ac i i y inc eased in he a ea o he eeding ough,
which indica es i s link wi h eeding beha io bu also in he lying a ea o he pen. He e o-
geneous dis ibu ions o ac i i y (ac i i y sco e), dis ibu ed h oughou he pen wi h no
ocal poin o ac i i y, can be obse ed in he hea map (shown in Figu e 3) om he pe iod
o he la e a e noon, which also shows highe ac i i y in he ecal a ea in he op igh
co ne . Howe e , he ac i i y sco e is no su icien o conclude wha ype o beha io is
being unde aken in each a ea o he pen.
Ag icul u e 2023,13, 1639 9 o 20
3.2. Accu acy Objec , T acking and Beha io Recogni ion
The objec de ec o was e alua ed using he alida ion spli o he pig de ec ion da ase ,
esul ing in a [email p o ec ed]:0.95 o 0.864. In he selec ed ideos, on a e age 10.6 o 11 pigs (wi h
a s anda d de ia ion o 0.64) we e de ec ed pe ame. The main challenge o he objec
de ec o was isual occlusion, i.e., when a pig was hidden om he iew o he came a by
o he pigs’ bodies. When mo e han 11 pigs we e de ec ed in a ame, he bounding boxes
wi h he lowes de ec ion con idence we e disca ded such ha 11 de ec ions emained.
To e alua e objec acking pe o mance, an e alua ion da ase o 14 1-minu e ideo
sequences was sampled om he eco dings and manually anno a ed wi h g ound- u h
acking in o ma ion. The acking pe o mance me ics calcula ed by compa ing he
acking esul s on hese ideos o he g ound- u h anno a ions a e epo ed in Table 2.
When applied o he selec ed ideos, he ackle s eached an a e age leng h o 18 min
be o e an ID swi ch o ackle agmen a ion, and he longes ackle s spanned o e
mul iple hou s. While his is no op imal (which would p oduce one 12-h ackle pe pig
pe day), he a e age ackle leng h was su icien o cap u e beha io al pa e ns consis ing
o mul iple, sequen ially execu ed ac i i ies.
Table 2.
Calcula ed acking pe o mance me ics a e aged o e all e alua ion sequences (MOTA:
mul iple objec acking accu acy, IDF1: global min-cos F1 sco e o ID associa ions, # Swi ches: o al
numbe o ack swi ches, # F agmen a ions: o al numbe o swi ches om acked o no acked).
MOTA IDF1 # Swi ches # F agmen a ions
A e age 0.982 0.985 0.5 1.286
S anda d De ia ion 0.032 0.017 0.76 1.541
A [email p o ec ed] (mean a e age p ecision) o 0.7365 was achie ed on he ac i i y
ecogni ion alida ion se by he ained ac i i y ecogni ion model. The a e age p ecision
o each ac i i y class is lis ed in Table 3. Si ing and s anding showed he lowes a e age
p ecision alues by a la ge ma gin. Visual inspec ion o esul s has shown ha hese ac ions
a e occasionally con used wi h lying by he model, due o simila i y in isual appea ance
and lack o mo emen . Good a e age p ecision alues we e achie ed o common ac i i ies
like lying (0.997), eeding (0.994), de eca ing (0.960) and playing (0.969).
Table 3. A e age p ecision pe ac i i y class as obse ed in model alida ion.
Ac i i y [email p o ec ed]
lying 0.997
si ing 0.374
s anding 0.296
mo ing 0.807
in es iga ing 0.793
eeding 0.994
de eca ing 0.960
playing 0.969
miscellaneous 0.439
3.3. Beha io Pa e ns and U iliza ion o he Pen
All he a o emen ioned beha io s we e acked h oughou he en i e obse a ion
pe iod. Video da a based beha io ecogni ion esul ed in p edic ions o di e en beha io
pa e ns. In Table 4, he desc ip i e s a is ics o beha io g ouped in o 10-min blocks can be
obse ed. These 10-min blocks p o ided a quan i ying o e iew o he di e se beha io al
pa e ns obse ed. The use o hese 10-min blocks allowed o a su icien ly de ailed
Ag icul u e 2023,13, 1639 16 o 20
4.2.3. Lying/Res ing
Compa able o Ekkel e al., (2003) and Ruckebush e al., (1972) [
38
,
45
], he indings in
his s udy highligh lying beha io as he dominan ac i i y, accoun ing o up o 80% o
he o al ime budge , while he pe cen age o lying dec eased du ing he la e hal o he
day. No conclusions can be d awn ega ding noc u nal lying ac i i ies, as he e alua ion
pe iod did no encompass nigh ime obse a ions. Lying beha io p edominan ly occu ed
wi hin he clus e ed lying a ea, indica ing a p e e ence o speci ic lying zones. Addi ional
spa ial dis ibu ion analysis could quan i y space equi emen s o all lying pigs, aking
in o accoun hei endency o lie down simul aneously o a subs an ial po ion o he day.
No ably, his s udy did no conside he lying pos u e o he pigs. Di e en ia ing be ween
lying pos u es which a e known o a y h oughou he day [
38
] could p o ide addi ional
insigh s in o clima e condi ions.
4.3. Beha io Sequences/P ocess Mining
The ocus o his s udy was o in es iga e gene al pa e ns o beha io occu ence and
succession a he han acking indi idual animals and hei comple e beha io sequence.
The e a e a ious app oaches a ailable o indi idual acking, including hose based on
ideo da a [
46
], as well as he u iliza ion o RFID (Radio F equency Iden i ica ion) echnol-
ogy. RFID chips o e an al e na i e means o ack animal encoun e s and in e ac ions wi h
objec s [
47
–
49
], which could p esen an in e es ing a enue as ID e i ica ion in combina ion
wi h acking based on ideo da a.
4.3.1. De eca ing
Pigs end o show speci ic beha io sequences like ea ing, d inking, u ina ing, and
de eca ing as no mal beha io . The gene al de eca ion beha io o g ouped housed pigs
in ol es sni ing be o e elimina ion in 50–70% o obse a ions and ollowed by mo ing
away immedia ely a e elimina ion [
42
]. Typical elimina ion sequences a e explo ing-
elimina ion-mo ing and mo ing-elimina ion-mo ing [
43
] o as desc ibed by Wechsle and
Bachmann (1998) [
50
] in de ail: en e he dunging a ea, sni , pos u e, de eca e/u ina e. Sim-
ila beha io al sequences can be obse ed in his s udy using p ocess mining o ideo da a.
Clus e ed p ocess models iden i ied he sequence in es iga ing-de eca ing-in es iga ing
oge he wi h in es iga ing-mo ing-in es iga ing as majo beha io sequences o he
de eca ion p ocess which is consis en wi h he li e a u e.
The analyses o de eca ion beha io and i s in eg a ion in o beha io sequences can
se e as c ucial ea ly wa ning ools o de ec ing anomalies in managemen
p ac ices [9,43]
.
De ia ions om he designa ed de eca ion a ea o al e a ions in de eca ion beha io wi hin
beha io sequences due o ex e nal dis up ions o speci ic en i onmen al condi ions
(e.g., clima ic ac o s) can be u ilized as aluable managemen indica o s. Moni o ing
and ecognizing such changes in de eca ion pa e ns may aid in iden i ying po en ial issues
in pig husband y and p o ide aluable insigh s o op imizing managemen s a egies o
ensu e animal wel a e and o e all sys em e iciency.
4.3.2. Feeding
The eeding beha io o a ening pigs is highly dependen on he managemen sys em.
In eeding managemen s compa able o his s udy (wi h es ic ed access o eed and
ixed eeding imes) pigs end o demons a e mo emen owa ds he eeding ough
in close empo al p oximi y o hei egula eeding schedule. Addi ionally, pigs a e
s imula ed by he g oup in mo emen and eeding [
51
]. Beha io sequences iden i ied
in he clus e ed p ocess models in his s udy highligh he beha io sequence o mo ing-
eeding, di ec link o eeding (Figu e 8a) o looped beha io o mo ing- eeding-mo ing (o
in es iga ing- eeding-in es iga ing da a no shown he e) in be ween wo lying pe iods. A
se e e amoun o eeding ac i i ies could be iden i ied be ween wo lying pe iods wi hou
addi ional mo ing o in es iga ing beha io . These di e en indings gene ally comply
wi h he assump ions om Signo e e al., (1975) [
51
]. This indica es ha he p ocess mining

Ag icul u e 2023,13, 1639 17 o 20
app oach can iden i y ele an beha io al sequences. De ia ions a e no iceable in cases
whe e eeding beha io di ec ly ollows lying beha io and hen ansi ions back in o
lying beha io . I is highly likely ha his pa e n is in luenced by he du a ion o ime a
speci ic beha io needs o be pe o med be o e i can be a ibu ed o a pa icula ca ego y.
Fine- uning he h eshold se ings o his du a ion could be one possibili y o enhance he
accu acy and obus ness o he models.
The applica ion o p ocess mining on ideo da a enables he iden i ica ion o beha io
sequences and holds po en ial o beha io al analysis unde a ious in luencing ac o s.
Mo eo e , i can be conside ed as an ea ly wa ning ool, capable o de ec ing de ia ions
om es ablished and ypical beha io sequences, acili a ing imely in e en ion and
imp o ed managemen p ac ices.
5. Conclusions
The s eps o he pa h om ac i i y ecogni ion in image da a o he new app oach
o iden i ica ion o beha io al sequences using p ocess mining o desc ibing beha io al
pa e ns in pigs exhibi simila app oaches o wha happens in he pig pen, albei wi h
inc easing le els o de ail. All esul s om he s eps o he pa h demons a e plausible
conclusions and can be measu ed wi h compa able app oaches. The p ocess models o
desc ibing beha io al sequences in pig beha io ep esen a no el app oach ha success-
ully iden i ies indi idual beha io al pa e ns and clus e s hem in o dis inc sequences.
Fu u e in-dep h analyses could depic addi ional beha io al sequences wi h di e en s a
and end beha io s o conside u he beha io al di e en ia ions. O e all, his me hod
p esen s a p omising app oach o he au oma ed assessmen o pig beha io and beha -
io al sequences based on ideo da a. The iden i ica ion o such beha io al sequences is
c ucial o unde s anding he na u al low o ac i i ies and he unde lying pa e ns in pig
beha io . This knowledge can in o m managemen decisions and shed ligh on po en ial
de ia ions o anomalies ha equi e a en ion. The ecogni ion and spa ial assignmen
o de eca ion beha io can be a aluable ool in educing wo kload and minimizing en i-
onmen al impac by ensu ing adhe ence o designa ed de eca ion a eas, pa icula ly in
eely en ila ed ba ns wi h s uc u ed unc ional a eas. Fu he in es iga ions a e needed
o ans e his me hod o s uc u ed mul i-a ea pens wi h o wi hou eely en ila ed
condi ions. Also addi ional e o s should ocus on e ining he p ocess mining app oach
by explo ing di e en h eshold se ings and inco po a ing addi ional a iables o imp o e
he models.
Au ho Con ibu ions:
Concep ualiza ion, A.M. and A.L.; me hodology, A.L. and J.B.; so wa e, A.L.
and J.B.; alida ion, A.M., A.K. and E.H.; o mal analysis, A.M.; in es iga ion, A.M.; esou ces, A.M.;
da a cu a ion, A.M, A.L. and J.B.; w i ing—o iginal d a p epa a ion, A.M. and A.L.; w i ing— e iew
and edi ing, A.M., A.L. and A.K.; isualiza ion, A.L. and J.B.; supe ision, A.K. and E.H.; p ojec
adminis a ion, A.M.; unding acquisi ion, A.M. and A.K. All au ho s ha e ead and ag eed o he
published e sion o he manusc ip .
Funding:
This p ojec has ecei ed unding om he S a e o Schleswig-Hols ein unde he Da en-
campus p ojec g an no. 220 21 016.
Ins i u ional Re iew Boa d S a emen :
E hical e iew and app o al we e wai ed o his s udy due
o he na u e o he da a collec ion p ocess. The ideo da a eco ding was conduc ed in no mal a m
condi ions, and no al e a ions o in e en ions we e made as pa o he s udy. As a esul , he e we e
no po en ial isks o ha ms o human o animal subjec s, and he s udy s ic ly adhe ed o e hical
guidelines and egula ions. Consequen ly, he animal wel a e o ice deemed ha an e hical e iew
and app o al p ocess was unnecessa y o his pa icula s udy.
Da a A ailabili y S a emen :
The aw ideo ma e ials a e no publicly a ailable due o copy igh
es ic ions. Da a including he esul s o objec de ec ion, objec acking, and ac i i y ecogni ion is
a ailable unde he ollowing e e ence: [
52
]. The code can be downloaded unde he ollowing link:
h ps://gi hub.com/a idle/ ideo-p ocess-mining-public (accessed on 18 Augus 2023).
Ag icul u e 2023,13, 1639 18 o 20
Acknowledgmen s:
We would like o hank he Schleswig-Hols ein Chambe o Ag icul u e, espe-
cially he Fu e kamp expe imen al ba n, o he oppo uni y o eco d he ideos and o p o iding
he accompanying pa ame e s.
Con lic s o In e es : The au ho s decla e no con lic o in e es .
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Disclaime /Publishe ’s No e:
The s a emen s, opinions and da a con ained in all publica ions a e solely hose o he indi idual
au ho (s) and con ibu o (s) and no o MDPI and/o he edi o (s). MDPI and/o he edi o (s) disclaim esponsibili y o any inju y o
people o p ope y esul ing om any ideas, me hods, ins uc ions o p oduc s e e ed o in he con en .