Về mô hình nhận dạng tư thế võ dựa trên ảnh chiều sâu

BáGI ODệCV OT O TRìNG I HC B CH KHOA H NáI V MHNHNH ND NGTìTH V DĩATR N NHCHI US U LU N NTI NS KßTHU T H Nºi 2020 I NT BáGI ODệCV OT O TRìNG I HC B CH KHOA H NáI V MHNHNH ND NGTìTH V DÜATR N NHCHI US U Ng nh: Kÿ thu“t M¢ s : 9520203 LU N NTI NS KòTHU T NGìI HìNG D N KHOA HC: TS Lả Dụng TS Ph⁄m Th nh Cỉng H Nºi 2020 i»n tß I NTÛ L˝I CAM OAN Tæi xin cam oan lu“n Ăn: "V mổ hnh nhn dng tữ th vê dỹa trản Ênh chiu sƠu" l cổng trnh nghiản cứu ca ri¶ng tỉi Mºt phƒn c¡c sŁ li»u, k‚t qu£ tr…nh b y lun Ăn l trung thỹc,  ữổc cỉng bŁ tr¶n c¡c t⁄p ch‰ khoa håc chuy¶n ng nh, k yu hi ngh khoa hồc nữợc v quc t Phn cặn li ca lun Ăn chữa ữổc cổng b bĐt ký cổng trnh nghiản cứu v ngo i nữợc H Ni, thĂng 01 nôm 2020 NGHI N CÙU SINH Nguy„n T÷íng Th nh T PTH HìNGD N TS Lả Dụng TS Phm Th nh Cổng i LIC MèN Lun Ăn tin sắ ữổc thỹc hiằn ti Viằn iằn tò Vin thổng, trữớng i hồc BĂch khoa H Ni dữợi sỹ hữợng dÔn khoa hồc ca TS L¶ Dơng v TS Ph⁄m Th nh Cỉng Nghi¶n cứu sinh xin b y tọ lặng bit ỡn sƠu sc tợi cĂc thy, cổ v 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Nhìn; Ban chı nhi»m Khoa Kÿ thu“t v Cæng ngh», gia …nh, b⁄n b v ỗng nghiằp  ng viản khch lằ, to mồi iu kiằn thun lổi NCS yản tƠm cổng t¡c v håc t“p H Nºi, th¡ng 01 n«m 2020 NGHI N CU SINH Nguyn Tữớng Th nh ii NáI DUNG L˝I CAM OAN L˝IC MÌN i ii N¸I DUNG v KÞHI UV VI TT T vi DANHS CHB NGBI U viii DANHS CHHNHV xiv M— U Ch÷ìng 1: T˚NG QUAN 12 1.1 Håc m¡y, håc s¥u v øng döng 1.1.1 Håc m¡y 1.1.2 Håc s¥u 1.2 H» thŁng khỉi phưc ho⁄t ºng cıa ngữới khổng gian 3-D v chĐm im vê thut 1.2.1 H» thŁng khỉi phưc ho⁄t ºng cıa ngữới khổng gian 3-D 1.2.2 Hằ thng chĐm i”m vª thu“t 1.3 ìợc lữổng khung xữỡng trản cỡ th ng÷íi khỉng gian 2-D 1.3.1 ìợc lữổng khung xữỡng trản Ênh m u 1.3.2 ìợc lữổng khung xữỡng trản Ênh s¥u 1.3.3 ìợc lữổng tữ th dỹa trản i tữổng v ng cÊnh hot ng 1.3.4 Nh“n x†t 1.4 ìợc lữổng khung xữỡng v tữ th‚ ng÷íi mỉi tr÷íng 3-D 1.4.1 Phửc hỗi tữ th 3-D ca ngữới t mºt £nh 1.4.2 Phửc hỗi tữ th 3-D cıa ng÷íi 1.4.2.1 Phửc hỗi khung xữỡng, tữ th ngữới khæng gian 3-D tł mºt £nh 1.4.2.2 Phöc hỗi khung xữỡng, tữ th ngữới khổng gian 3-D tł mºt chuØi £nh 1.4.3 Nh“n x†t 1.5 C¡c bº cì sð d liằu cho viằc Ănh giĂ ữợc lữổng khung xữỡng khæng gian 3-D 1.5.1 Giỵi thi»u Kinect 1.5.2 Hi»u ch¿nh dœ li»u thu tł c£m bi‚n Kinect 1.6 TŒng k‚t ch÷ìng iii 12 12 14 16 16 16 16 17 18 21 22 23 23 24 25 25 25 31 31 31 37 Chữỡng 2: ìC LìẹNG KHUNG XìèNG CếA NG×˝I TØ DÚ LI U Vˆ C˚ TRUY N TRONG KHNG GIAN 3-D 2.1 ìợc lữổng khung xữỡng khổng gian 2-D 2.1.1 Giỵi thi»u 2.1.2 C¡c nghi¶n cøu li¶n quan 2.1.3 Sß dưng håc sƠu cho viằc ữợc lữổng cĂc h nh ng b i vê c truyn khổng gian 2-D 2.1.3.1 Ph÷ìng thøc 2.1.3.2 Cì sð dœ li»u c¡c b i vê c truyn 2.1.3.3 Ph÷ìng thøc ¡nh gi¡ 2.1.3.4 Xoay v dàch dœ li»u khæng gian 3-D 2.1.3.5 Kt quÊ ữợc lữổng v nh“n x†t 2.1.4 K‚t lu“n 2.2 Phửc hỗi khung xữỡng, tữ th ngữới khổng gian 3-D v b che khuĐt 2.2.1 Giợi thiằu 2.2.2 C¡c nghi¶n cøu li¶n quan 2.2.3 Phửc hỗi khung x÷ìng, t÷ th‚ ng÷íi khỉng gian 3-D 2.2.3.1 Nghiản cứu so sĂnh v khổi phửc khung x÷ìng ng÷íi khỉng gian 3-D 2.2.3.2 Th‰ nghi»m v k‚t qu£ ÷ỵc l÷ỉng khung x÷ìng 3-D 2.2.3.3 K‚t lu“n 2.2.4 ìợc lữổng khung xữỡng, tữ th ngữới b che khuĐt 2.3 TŒng k‚t ch÷ìng Ch÷ìng 3: NH ND NGV CH M I M ¸NGT CVˆC˚ TRUY N VI T NAM 38 39 39 40 43 43 47 53 56 61 64 74 74 74 77 78 82 84 85 92 93 3.1 Giỵi thi»u 93 3.2 C¡c nghi¶n cøu li¶n quan 3.3 Cỡ s lỵ thuyt nh“n di»n ºng t¡c t§n cỉng v ch§m i”m ºng tĂc vê 3.3.1 Nhn diằn ng tĂc tĐn cổng 3.3.1.1 Xß lỵ d liằu 3.3.1.2 Tr‰ch xu§t °c trững cỡ th ngữới vợi camera Kinect 3.3.2 Mổ hnh chĐm im ng tĂc vê c truyn 3.3.2.1 Mỉ t£ ºng t¡c ng÷íi 3.3.2.2 Cỉng thøc ch§m i”m 3.4 Thüc nghi»m 3.4.1 Nh“n di»n ºng t¡c t§n cỉng 96 97 97 97 97 102 102 105 107 107 iv 3.5 3.6 3.4.1.1 Nhn diằn ng tĂc tĐn cổng bng cƠy ph¥n lo⁄i 107 3.4.1.2 Nh“n di»n ºng t¡c t§n cỉng b‹ng m⁄ng nì ron 108 3.4.2 ChĐm im ng tĂc vê c truyn Vi»t Nam 110 K‚t lu“n 115 TŒng k‚t ch÷ìng 115 K TLU NV HײNGPH TTRI N DANH MệC C C CNG TR NHCNG Bă CÕA LU N N 117 115 T ILI UTHAMKH O 120 PHƯ LƯC 134 v DANHMƯCC CKÞHI UV VI TT T SŁ Vi‚t t›t Gi£i ngh¾a Ngh¾a ti‚ng Vi»t AD AP Average deviation Average Precision º l»ch trung b…nh º ch‰nh x¡c trung b…nh APMArticulated Part-based Modeldeviation Mổ hnh dỹa trản phn khợp ni CPM CPU Convolutional Pose Machines Central Processing Unit M¡y håc cß ch tch chp ỡn v xò lỵ trung tƠm CNN Convolutional Nerural Network M⁄ng Nì ron t‰ch ch“p CNNs Convolutional Nerural Networks M⁄ng Nì ron t‰ch ch“p nhi•u lỵp DPM Deformable Part Model Mỉ h…nh phƒn bi‚n d⁄ng DTW Dynamic Time Warping So khỵp chi thíi gian ºng 10 DV Digital Video Video sŁ 11 fps frame per second Khung hnh trản giƠy 12 GPU Graphics Processing Unit ỡn v xò lỵ ỗ hồa 13 HMMs Hidden Markov Models Mæ h…nh Markov 'n 14 HOG Histogram of Oriented Gradients Biu ỗ hữợng dc 15 HRNet High-Resolution Network M⁄ng º ph¥n gi£i cao 16 IR InfraRed camera MĂy Ênh hỗng ngoi 17 JI Jaccard Index Ch s Jaccard 18 LSTM Long Short-Term Memory M⁄ng bº nhỵ ng›n nh hữợng 19 MADS Martial Arts, Dancing and Sports d i hn Vê c truyn, khiảu vụ, th thao 20 MOCAP MOtion CAPture Thu nh“n chuy”n ºng 21 MPJPE MeanPerJointPositionError º o sai sŁ trung b…nh cıa c¡c 22 MS MicroSoft khỵp nŁi Microsoft 23 MSE Mean Squared Error Sai sŁ b…nh ph÷ìng 24 OCR Optical Character Recognition Nh“n d⁄ng kỵ tỹ quang hồc 25 OKS Object Key point Similarity º t÷ìng tü c¡c i”m ⁄i di»n 26 OpenCV Open Computer Vision Thữ viằn m nguỗn m th giĂc 27 OpenNI Open Natural Interaction m¡y t‰nh Th÷ vi»n hØ trỉ a ngỉn ngœ 28 PCA Principal Component Analysis Ph¥n t‰ch nguyản lỵ th nh phn vi 29 PCL 30 RAM Poind Cloud Library Random Access Memory Thữ viằn Ăm mƠy im B nhợ truy nhp ngÔu nhiản 31 RDF Random Decision Forests Rng quyt nh ngÔu nhiản 32 RGB Red Green Blue ä Xanh l¡ Xanh lì 33 SDK Software Development Kit Kit ph¡t tri”n phƒn m•m 34 SVM Support Vector Machine Håc m¡y hØ træ vector 35 TOF Time-Of-Flight sensor C£m bi‚n TOF 36 V1 Version Phi¶n b£n 37 V2 Version Phi¶n b£n 38 VE Vector Estimation Vector dü o¡n 39 VG Vector Ground truth Vector Ănh dĐu thỹc 40 VNMA VietNam Martial Arts Vê cŒ truy•n Vi»t Nam vii DANHS CHB NGBI U B£ng 1.1 Thng kả cĂc nghiản cứu ữợc lữổng khung xữỡng cıa ng÷íi khỉng gian 3-D m câ ¡nh gi¡ tr¶n cì cð dœ li»u Human3.6M [86] v k‚t qu£ ÷ỵc l÷ỉng BÊng 1.2 KhÊo sĂt v ữợc lữổng tữ th ngữới khổng gian 3-D sò dửng 27 Ênh B£ng 1.3 Kh£o s¡t v ữợc lữổng khung xữỡng ngữới khổng gian 3-D 29 tł mºt chuØi £nh B£ng 2.1 SŁ khung h…nh cĂc tữ th vê ca cỡ s d liằu VNMA 30 50 B£ng 2.2 SŁ khung hnh cĂc tữ th vê ca cỡ s d li»u SVNMA 51 B£ng 2.3 K‚t qu£ trung bnh ca ữợc lữổng cĂc khợp ni (AP), gõc lằch giœa c¡c khỵp cıa dœ li»u gŁc v c¡c khỵp ni ữợc lữổng ữổc (AD) v khoÊng cĂch gia cĂc trung bnh gia cĂc im i diằn ữợc lữổng ữổc v c¡c i”m ⁄i di»n cıa dœ li»u gŁc, t÷ìng øng vỵi 61 BÊng 2.4 Kt quÊ ữợc lữổng khung xữỡng trản £nh v chi‚u sang khỉng gian 3-D vỵi 14 i”m xữỡng trản d liằu VNMA Kt quÊ ữổc Ănh giĂ tr¶n º o MPJPE theo ìn milimet (mm) B£ng 2.5 SŁ khung h…nh ¡nh gi¡ dœ li»u VNMA 68 69 BÊng 2.6 Kt quÊ ữợc lữổng khung xữỡng trản Ênh sau â chi‚u sang khỉng gian 3-D tr¶n cì sð d liằu MADS vợi 14 im xữỡng B£ng 2.7 SŁ khung hnh cho viằc Ănh giĂ ữợc lữổng khung xữỡng tr¶n £nh 71 sai â chi‚u sang khỉng gian 3-D tr¶n cì sð dœ li»u MADS BÊng 2.8 Kt quÊ ữợc lữổng khung xữỡng trản Ênh sau õ chiu sang khổng 72 gian 3-D trản cỡ s d liằu VNMA vợi 15 im x÷ìng BÊng 2.9 Kt quÊ ữợc lữổng khung xữỡng trản Ênh sau â chi‚u sang khỉng 88 gian 3-D tr¶n cì s d liằu MADS vợi 15 im xữỡng B£ng 3.1 Th” hi»n t¡m v†c tì chi viii 89 104 T ILI UTHAMKH O [1] MJ Rantz, TS Banerjee, E Cattoor, SD Scott, M Skubic, and M Popescu Auto-mated fall detection with quality improvement "rewind" to reduce falls in hospital rooms J Gerontol Nurs, 40(1):13 17, 2014 [2] Yury Degtyarev Philip L Davidson Sean Ryan Fanello Adarsh Kowdle Sergio Orts Christoph Rhemann David Kim Jonathan Taylor Pushmeet Kohli Vladimir Tankovich Shahram Izadi Mingsong Dou, Sameh Khamis Fusion4D: real-time performance capture of challenging scenes ACM Transactions on Graphics, 35(4), 2016 [3] 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