Preprocessing and Normalization of 3D-Skeleton-Data for Human Motion Recognition

Vox, Jan P.; Wallhoff, Frank

doi:10.1109/lsc.2018.8572153

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…So'nggi yillarda inson harakatining videotahlili kompyuter ko'rish sohasidagi tadqiqotlarning ajralmas qismiga aylandi va hozirgi vaqtda bunday tizimlarni rivojlantirishning ilg'or yo'nalishi hisoblanadi. Kompyuterni ko'rish vositalariga va inson harakatlarini tahlil qilishga qiziqish, xususan, paydo bo'ladigan keng imkoniyatlar: xavfsizlik tizimlarini tashkil etish, sport natijalarini tahlil qilish, odam-mashina interfeysi, video kuzatuv va videokonferentsiya bilan bog'liq [1][2][3][4][5][6][7].…”

Section: Kirishunclassified

“…Klassik ko'p obyektni kuzatish usullari klaster tahlilining qat'iy algoritmlarini (masalan, Ko'rtacha algoritmi) ishlatadi, ular yuqori hisoblash murakkabligi va natijada katta resurs xarajatlari bilan bog'liq [8]. LiDAR tizimi yordamida harakatni aniqlashning yaqinda rivojlanishini taqdim etadi [4]. Hozirgi vaqtda bu harakatni tahlil qilishning eng maqbul variantlaridan biri, ammo u odamlarni aniqlash uchun ishlatilmaydi, chunki bunday foydalanish uchun apparat xarajatlari qoplanmaydi.…”

Section: Kirishunclassified

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Skelet modeli asosida kompyuter ko‘rish tizimlarida inson harakatini tekshirish

Beknazarova,

Jaumitbaeva

2024

Международный журнал теоретических и прикладных вопросов цифров

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Kompyuter ko‘rish tizimlarida inson harakatini tahlil qilish ikki o‘lchovli va uch o‘lchovli fazolarda amalga oshiriladi. Ikki o‘lchovli fazoda qiyofalarni tahlil qilishning o‘ziga xos xususiyati bitta kameradan yoki bir nechta tana sensorlaridan olingan tasvirlarni qo‘llashdan iborat. Bunday yondashuv xatoning tez to‘planishiga olib keladi va natijada rasmning aniqligi past bo‘ladi. Uch o‘lchovli tahlilni amalga oshirishda bir nechta kameralar qo‘llaniladi va qiyofalar hajmli elementlar to‘plami sifatida taqdim etiladi. Uch o‘lchovli tahlilning yuqori aniqligiga qaramay, u yuqori hisoblash murakkabligi va ichki kompyuter tarmog‘idagi yuk bilan bog‘liq. Tadqiqot maqsadi bitta kamera tasviridan foydalangan holda modelni ishlab chiqishdir. Ko‘rib chiqilayotgan modelda inson qiyofasi sifatida tasvirlangan skelet, tasvirlangan assegmatik grafga asoslab hisoblash. Inson qiyofalarning tahlil tuzilmalari bajarildi va o‘n besh asosiy ball ta’kidlangan. Jismoniy va mantiqiy aloqalarning matematik tavsifi berilgan va tadqiqot qilingan. Nuqtalar va ulanishlarning tezligi va fazoviy xarakteristikalari harakatning umumiy dinamikasini tavsiflaydi. Inson harakati modelining tavsifi va uning muayyan tasvirlar uchun qurish modeli keltirilgan. Taklif etilayotgan model asosida grafik tugunlari va qirralarining nisbiy joylashuvi va tezlik xarakteristikalari haqidagi ma’lumotlarni to‘plash va tahlil qilish algoritmi ishlab chiqilgan. Ishlab chiqilgan model inson harakatining etalon (normal) dinamikasi haqida ma’lumot to‘plash uchun ishlatilishi mumkin. Agar standartdan chetga chiqadigan dinamika aniqlansa, xatti-harakatlar deviant sifatida aniqlanadi. Taklif etilayotgan algoritm inson harakatini aniqlash va tahlil qilish, shuningdek, harakatlarning mos yozuvlardan og‘ishlarini aniqlash uchun kompyuter ko‘rish tizimlarida qo‘llanilishi mumkin.

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Section: Kirishunclassified

Skelet modeli asosida kompyuter ko‘rish tizimlarida inson harakatini tekshirish

Beknazarova,

Jaumitbaeva

2024

Международный журнал теоретических и прикладных вопросов цифров

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“…where (p, q) runs across all links E j in the kinematic chain from the root node n 0 to the child node n j . To make the system invariant to translation and scaling, the input skeletons to both systems are previously centered around their right foot (to preserve as much skeleton variance information as possible [24]) and scaled down to be within the range [0, 1]. We denote this centering and normalization operation as N , and it is applied to each individual joint n j :…”

Section: A Normalization Of Input Variablesmentioning

confidence: 99%

Koopman pose predictions for temporally consistent human walking estimations

Mitjans¹,

Levine²,

Awad³

et al. 2022

Preprint

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We tackle the problem of tracking the human lower body as an initial step toward an automatic motion assessment system for clinical mobility evaluation, using a multimodal system that combines Inertial Measurement Unit (IMU) data, RGB images, and point cloud depth measurements. This system applies the factor graph representation to an optimization problem that provides 3-D skeleton joint estimations. In this paper, we focus on improving the temporal consistency of the estimated human trajectories to greatly extend the range of operability of the depth sensor. More specifically, we introduce a new factor graph factor based on Koopman theory that embeds the nonlinear dynamics of several lower-limb movement activities. This factor performs a two-step process: first, a custom activity recognition module based on spatial temporal graph convolutional networks recognizes the walking activity; then, a Koopman pose prediction of the subsequent skeleton is used as an a priori estimation to drive the optimization problem toward more consistent results. We tested the performance of this module on datasets composed of multiple clinical lowerlimb mobility tests, and we show that our approach reduces outliers on the skeleton form by almost 1 m, while preserving natural walking trajectories at depths up to more than 10 m.

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“…First the vector lengths between hip and ankle, knee and ankle and hip and knee—or

and

—need to be calculated using Equation ( 1 ). Subsequently, the angle

in position a and thereby the flexion in the knee joint can be determined using Equation ( 2 ) [ 27 , 28 ].

…”

Section: Data Processingmentioning

confidence: 99%

An Evaluation of Motion Trackers with Virtual Reality Sensor Technology in Comparison to a Marker-Based Motion Capture System Based on Joint Angles for Ergonomic Risk Assessment

Vox

Weber

Wolf

et al. 2021

Sensors

Self Cite

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The reproduction and simulation of workplaces, and the analysis of body postures during work processes, are parts of ergonomic risk assessments. A commercial virtual reality (VR) system offers the possibility to model complex work scenarios as virtual mock-ups and to evaluate their ergonomic designs by analyzing motion behavior while performing work processes. In this study a VR tracking sensor system (HTC Vive tracker) combined with an inverse kinematic model (Final IK) was compared with a marker-based optical motion capture system (Qualisys). Marker-based optical motion capture systems are considered the gold standard for motion analysis. Therefore, Qualisys was used as the ground truth in this study. The research question to be answered was how accurately the HTC Vive System combined with Final IK can measure joint angles used for ergonomic evaluation. Twenty-six subjects were observed simultaneously with both tracking systems while performing 20 defined movements. Sixteen joint angles were analyzed. Joint angle deviations between ±6∘ and ±42∘ were identified. These high deviations must be considered in ergonomic risk assessments when using a VR system. The results show that commercial low-budget tracking systems have the potential to map joint angles. Nevertheless, substantial weaknesses and inaccuracies in some body regions must be taken into account. Recommendations are provided to improve tracking accuracy and avoid systematic errors.

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Preprocessing and Normalization of 3D-Skeleton-Data for Human Motion Recognition

Cited by 7 publications

References 8 publications

Skelet modeli asosida kompyuter ko‘rish tizimlarida inson harakatini tekshirish

Skelet modeli asosida kompyuter ko‘rish tizimlarida inson harakatini tekshirish

Koopman pose predictions for temporally consistent human walking estimations

An Evaluation of Motion Trackers with Virtual Reality Sensor Technology in Comparison to a Marker-Based Motion Capture System Based on Joint Angles for Ergonomic Risk Assessment

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