IWT TETRA TOBCAT Industrial applications of object categorization techniques. Steven Puttemans

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1 Prgram 12:30 Registratin & cffee 13:00 Welcme and intrductin (Tn Gedemé) 13:15 Results frm the TOBCAT prject (Steven Puttemans) 14:00 Practical applicatins that use cmputer visin algrithms and their internal wrkflw. (FLIR ITS) 14:15 First set f dem pitches 14:45 Break at the dem fair 15:30 Results frm the 3D4SURE prject (Bas Altena, Jasper Wisbecq) 16:15 Ge-Airflight, "Successes up till nw" (GeInfra) 16:30 Secnd set f dem pitches 17:00 Dem fair and refreshments

2 IWT TETRA TOBCAT Industrial applicatins f bject categrizatin techniques Steven Puttemans

3 A wrap-up 1. Research gal 2. Object recgnitin bject categrizatin 3. Vila & Jnes bject categrizatin framewrk 4. Other framewrks tested 5. TOBCAT user cases 6. Cnclusins

4 A wrap-up 1. Research gal 2. Object recgnitin bject categrizatin 3. Vila & Jnes bject categrizatin framewrk 4. Other framewrks tested 5. TOBCAT user cases 6. Cnclusins

5 Research gal TETRA prject = technlgy transfer Gal is t transfer knwledge and technlgy frm academic research twards the industry Cmbined with user grup f industrial cmpanies Advice and guidance during the prject First access t prject results and achievements 92,5% - financed by IWT / 7,5% c-financed by cmpanies

6 Research gal TOBCAT : industrial applicatins f bject categrizatin Can we intrduce state-f-the-art bject categrizatin (detectin) algrithms in industrial applicatins with an easy-t-use interface? Using these techniques, can we slve practical cmputer visin prblems fr these industrial applicatins? (And achieve better results than basic prcessing techniques) Prject partners:

7 A wrap-up 1. Research gal 2. Object recgnitin bject categrizatin 3. Vila & Jnes bject categrizatin framewrk 4. Other framewrks tested 5. TOBCAT user cases 6. Cnclusins

8 Object recgnitin bject categrizatin Object recgnitin Object identificatin Object categrisatin Object classificatin????

9 Object recgnitin bject categrizatin Object categrizatin fcus n a cmplete bject class Variatin in the class itself: cars, cws, Overal appearance f the bject is still the same Cars 4 wheels frnt and side windws - Cws 4 legs rbust rectangular bdy -

10 Object recgnitin bject categrizatin Object categrizatin gets harder the mment there is mre and mre variatin intrduced Challenges fr rbustness: illuminatin, bject pse, clutter, cclusins, intra class appearance, viewpint,

11 Object recgnitin bject categrizatin Object categrizatin general apprach Learning frm examples f class instances Use the learned mdel fr detecting new bjects

12 A wrap-up 1. Research gal 2. Object recgnitin bject categrizatin 3. Vila & Jnes bject categrizatin framewrk 4. Other framewrks tested 5. TOBCAT user cases 6. Cnclusins + future wrk

13 Vila & Jnes bject categrizatin framewrk Open surce framewrk in OpenCV Famus f face lcatin in cnsumer pht cameras P. Vila and M. Jnes, Rapid Object Detectin using a Bsted Cascade f Simple Features, IEEE Cnference n Cmputer Visin and Pattern Recgnitin, 2001

14 Vila & Jnes bject categrizatin framewrk Used apprach Transfrm image t integral image On that image Calculate pssible features HAAR Wavelets / Lcal Binary Patterns Put all features in a bsting prcess Machine learning technique AdaBst apprach

15 Vila & Jnes bject categrizatin framewrk Used apprach CASCADE: cmbining weak classifiers int a strng classifier Sliding windw apprach fr feature calculatin n a single image

16 A wrap-up 1. Research gal 2. Object recgnitin bject categrizatin 3. Vila & Jnes bject categrizatin framewrk 4. Other framewrks tested 5. TOBCAT user cases 6. Cnclusins

17 Other framewrks that were tested Dallal & Triggs HOG fr human detectin HOG + SVM

18 Other framewrks that were tested Felzenszwalb Cascade bject detectin with defrmable parts AKA LatentSVM.

19 Other framewrks that were tested Dllár Integral channel features

20 A wrap-up 1. Research gal 2. Object recgnitin bject categrizatin 3. Vila & Jnes bject categrizatin framewrk 4. Other framewrks tested 5. TOBCAT user cases 6. Cnclusins

21 CASES EAVISE TEST CASES The setup used t perfrm ur first tests Allied Visin Tech manta GigE camera Kaiser RS1 camera stand

22 CASES EAVISE TEST CASES First attempt at a ckie detectr Cascade classifier LBP features Multiple backgrunds Very limited training set 40 psitive samples 250 negative samples Even unknwn backgrunds wrk

23 CASES EAVISE TEST CASES First attempt at a ckie detectr Even sme tests with SVM + HOG Dallal and Triggs apprach fr bject detectin

24 CASES EAVISE TEST CASES Then a candy detectr Cascade classifier LBP Again limited training data cmpared t cmmercial mdels Only single rientatin pssible previus case had rtatin invariant bjects.

25 360 degrees 5 degree bins nt real time prcessing 360 degrees 36 degree bins CASES EAVISE TEST CASES Then a candy detectr Orientatin limits f a single mdel degrees Rtatin invariant wrkarund

26 CASES EAVISE TEST CASES Face detectin and skin clr based segmentatin VJ face detectr HSV clr space Average value Dynamic threshld Full bdy persn detectin in academic cntext

27 CASES EAVISE TEST CASES Traffic sign detectin Lw training time 45 minutes! Pririty sign 70 km speed sign

28 CASE AdViSe Detectin f walking aids fr elderly peple Day and night ccurrences f bject Optimized trajectries Separate mdels Interest regin mask

29 Adding extra restrictins t the algrithm CASE AdViSe Detectin f walking aids fr elderly peple First case with advanced validatin f the algrithm Precisin Recall curves + trajectry fitting FORWARD mdel RETURNING mdel Green = best detectr / Blue = wrst detectr

30 CASE Aris BV Classificatin f Orchid flwer classes Actually nt really bject detectin, mre classificatin Used an SVM + clr features based apprach

31 CASE Aris BV Classificatin f Orchid flwer classes Aris had a detectin system We tried t d better with ur generated apprach Can be effectively cmbined with classificatin apprach

32 CASE Bibest Classificatin f mites micrscpic rganisms Base fr a master thesis see USB stick Technique can handle mtin blur! Orientatin still prblem rientatin filters!

33 CASES EUROSENSE Detectin f bject classes in aerial imagery Objects can be in any rientatin Example classes: cars, rad markings, swimming pls, railrad tracks, slar panels, Base case fr the dminant gradient apprach besides building bject detectin mdels

34 CASE EUROSENSE Detectin f bject classes in aerial imagery Objects can be in any rientatin Base case fr the dminant gradient apprach besides building bject detectin mdels Ideal fr rad markings! Very linear structures!

35 CASE EUROSENSE Detectin f bject classes in aerial imagery Aerial car detectr

36 CASE Case New Hlland Detectin f mwing verburden when harvesting grain Typical situatin f all pssible rientatins Dminant gradient apprach

37 CASE Case New Hlland Detectin f mwing verburden when harvesting grain Detectr still missing Suggested apprach Take small patch samples f the input image Learn a classifier n verburden specific patches Classify each patch Merge patches that are classified verburden and that are clse tgether PROBLEM = regin will be larger than segmentatin Apply integral channel apprach

38 CASE Vansteelandt - Gevisat Detectin f bjects in panramic images and blurring fr privacy reasns 360 degree panramic images Can we detect pedestrians with high accuracy? Applied n cars (number plates) and pedestrians (faces) Bth pen surce OpenCV functins and EAVISE prduced detectin algrithms

39 CASE Vansteelandt - Gevisat Detectin f bjects in cyclramic images and blurring fr privacy reasns Detectins everywhere false psitive detectins Slved with scale lcatin relatin mapping Relatin ut training data Applied n actual detectin data

40 CASE Vansteelandt - Gevisat Detectin f bjects in cyclramic images and blurring fr privacy reasns Only valid detectins remain Pssibility t lwer scre threshld

41 CASE Vansteelandt - Gevisat Detectin f bjects in cyclramic images and blurring fr privacy reasns Pedestrians n bike use bycicle detectr New scale lcatin relatin shuld be learned! Increase certainty f fund detectins (blue arrw)

42 CASE Vansteelandt - Gevisat Detectin f bjects in cyclramic images and blurring fr privacy reasns Last prblem traffic ples & rundabut signs Slutins Naïve Bayes classifier Feature vectr Naïve Bayes Test set YMC clr channel

43 CASE Grntmij Evalutin f different persn detectrs n cyclramic data Vila & Jnes framewrk AdaBst Face detectr n HAAR Wavelets - red Full bdy detectr n HOG features - green

44 CASE Grntmij Evalutin f different persn detectrs n cyclramic data Vila & Jnes full bdy detectr cmbined with scre threshlding new feature in OpenCV OpenCV LatentSVM 1.0 part based mdeling

45 CASE Grntmij Evalutin f different persn detectrs n cyclramic data Felzenswalb LatentSVM 4.0 Own trained upper bdy detectr

46 CASE Grntmij Evalutin f different persn detectrs n cyclramic data Dllar ICF framewrk Integral channel apprach multiple input surces ALWAYS keep in mind yu need t chse a threshld!

47 CASE Grntmij Evalutin f different persn detectrs n cyclramic data Scale space relatin enfrcing applied

48 CASE Grntmij & Vansteelandt Effective blurring f detected bjects / pedestrians Simply applying a blur creates disturbing bundary effects which are nt desirable fr further prcessing Fr example perfrming measurements Changed t a sft blurring filter apprach

49 CASE Grntmij & Vansteelandt

50 CASE Grntmij & Vansteelandt

51 CASE Traficn FLIR ITS Detectin and tracking f pedestrians in IR images Analysis f traffic scenes Tw master theses: Julien Mathieu & Jeren Prvst

52 A wrap-up 1. Research gal 2. Object recgnitin bject categrizatin 3. Vila & Jnes bject categrizatin framewrk 4. Other framewrks tested 5. TOBCAT user cases 6. Cnclusins

53 Cnclusins Prject clearly shwed Object categrizatin techniques can d a lt! Way mre pssibilities than segmentatin and manual threshld based techniques. Steep learning curve fr cmpanies t implement them. Many pen surce platfrms ut there, like OpenCV Mre advanced techniques nt yet implemented Hwever nt all that well dcumented Can they ensure a stable piece f sftware? Object categrizatin / detectin can definitely suit the needs f industrial applicatins.

54 Cnclusins Sme remarks Training data Chse data wisely Cnsider the applicatin where the detectr has t run Taking lts f data just t be sure dumb apprach Make use f scene and applicatin cnstraints Fixed camera psitin results mstly in fixed scales Knwn mvement segment interesting regins Camera captures mre than needed apply masking Try t avid manual threshlding Use the pwer f machine learning techniques Little extra effrt = much mre stable algrithm

55 Thanks t Thanks t the IWT fr prviding the funding f this prject. We wuld definitely like t thank all the participating academic and industrial partners f this prject:

56 Mre inf abut the TOBCAT prject? Steven Puttemans: Tn Gedemé: Website:

57 Prgram 12:30 Registratin & cffee 13:00 Welcme and intrductin (Tn Gedemé) 13:15 Results frm the TOBCAT prject (Steven Puttemans) 14:00 Practical applicatins that use cmputer visin algrithms and their internal wrkflw. (FLIR ITS) 14:15 First set f dem pitches 14:45 Break at the dem fair 15:30 Results frm the 3D4SURE prject (Bas Altena, Jasper Wisbecq) 16:15 Ge-Airflight, "Successes up till nw" (GeInfra) 16:30 Secnd set f dem pitches 17:00 Dem fair and refreshments

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