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Wyszukujesz frazę "pose estimation" wg kryterium: Temat


Wyświetlanie 1-7 z 7
Tytuł:
An accurate and stable pose estimation method for planar cases considering the line constraints between every two points
Autorzy:
Zhang, Zimiao
Zhang, Hao
Zhang, Fumin
Zhang, Shihai
Powiązania:
https://bibliotekanauki.pl/articles/27311739.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
pose estimation
line constraints
coplanar
non-iterative
Opis:
The current solutions for pose estimation problems using coplanar feature points (PnP problems) can be divided into non-iterative and iterative solutions. The accuracy, stability, and efficiency of iterative methods are unsatisfactory. Therefore, non-iterative methods have become more popular. However, the non-iterative methods only consider the correspondence of the feature points with their 2D projections. They ignore the constraints formed between feature points. This results in lower pose estimation accuracy and stability. In this work, we proposed an accurate and stable pose estimation method considering the line constraints between every two feature points. Our method has two steps. In the first step, we solved the pose non-iteratively, considering the correspondence of the 3D feature points with their 2D projections and the line constraints formed by every two feature points. In the second step, the pose was refined by minimizing the re-projection errors with one iteration, further improving accuracy and stability. Simulation and actual experiment results show that our method’s accuracy, stability, and computational efficiency are better than the other existing pose estimation methods. In the -45° to +45° measuring range, the maximum angle measurement error is no more than 0.039°, and the average angle measurement error is no more than 0.016°. In the 0 mm to, 30 mm measuring range, the maximum displacement measurement error is no more than 0.049 mm, and the average displacement measurement error is no more than 0.012 mm. Compared to other current pose estimation methods, our method is the most efficient based on guaranteeing measurement accuracy and stability.
Źródło:
Metrology and Measurement Systems; 2023, 30, 2; 235--258
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A lightweight multi-person pose estimation scheme based on Jetson Nano
Autorzy:
Liu, Lei
Blancaflor, Eric B.
Abisado, Mideth
Powiązania:
https://bibliotekanauki.pl/articles/30148243.pdf
Data publikacji:
2023
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
human pose estimation
lightweight model
Edge AI
deep learning
computer vision
Opis:
As the basic technology of human action recognition, pose estimation is attracting more and more researchers' attention, while edge application scenarios pose a higher challenge. This paper proposes a lightweight multi-person pose estimation scheme to meet the needs of real-time human action recognition on the edge end. This scheme uses AlphaPose to extract human skeleton nodes, and adds ResNet and Dense Upsampling Revolution to improve its accuracy. Meanwhile, we use YOLO to enhance AlphaPose’s support for multi-person pose estimation, and optimize the proposed model with TensorRT. In addition, this paper sets Jetson Nano as the Edge AI deployment device of the proposed model and successfully realizes the model migration to the edge end. The experimental results show that the speed of the optimized object detection model can reach 20 FPS, and the optimized multi-person pose estimation model can reach 10 FPS. With the image resolution of 320×240, the model’s accuracy is 73.2%, which can meet the real-time requirements. In short, our scheme can provide a basis for lightweight multi-person action recognition scheme on the edge end.
Źródło:
Applied Computer Science; 2023, 19, 1; 1-14
1895-3735
2353-6977
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Combined YOLOv5 and HRNet for high accuracy 2D keypoint and human pose estimation
Autorzy:
Nguyen, Hung-Cuong
Nguyen, Thi-Hao
Nowak, Jakub
Byrski, Aleksander
Siwocha, Agnieszka
Le, Van-Hung
Powiązania:
https://bibliotekanauki.pl/articles/2147147.pdf
Data publikacji:
2022
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
YOLOv5
HRNet
2D key points estimation
2D human pose estimation
Opis:
Two-dimensional human pose estimation has been widely applied in real-world applications such as sports analysis, medical fall detection, human-robot interaction, with many positive results obtained utilizing Convolutional Neural Networks (CNNs). Li et al. at CVPR 2020 proposed a study in which they achieved high accuracy in estimating 2D keypoints estimation/2D human pose estimation. However, the study performed estimation only on the cropped human image data. In this research, we propose a method for automatically detecting and estimating human poses in photos using a combination of YOLOv5 + CC (Contextual Constraints) and HRNet. Our approach inherits the speed of the YOLOv5 for detecting humans and the efficiency of the HRNet for estimating 2D keypoints/2D human pose on the images. We also performed human marking on the images by bounding boxes of the Human 3.6M dataset (Protocol #1) for human detection evaluation. Our approach obtained high detection results in the image and the processing time is 55 FPS on the Human 3.6M dataset (Protocol #1). The mean error distance is 5.14 pixels on the full size of the image (1000×1002). In particular, the average results of 2D human pose estimation/2D keypoints estimation are 94.8% of PCK and 99.2% of PDJ@0.4 (head joint). The results are available.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 4; 281--298
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Object pose estimation in monocular image using modified FDCM
Autorzy:
Dabbour, Abd Alrazzak
Habib, Rabie
Saii, Mariam
Powiązania:
https://bibliotekanauki.pl/articles/305648.pdf
Data publikacji:
2020
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
3DOF pose estimation
FDCM
monocular image
Voronoi diagram
line-based matching
LSD
Opis:
In this paper, a new method for multi-object detection and pose estimation in a monocular image is proposed based on the FDCM method. This method can detect an object with a high-speed running time even if the object was under partial occlusion or bad illumination. Additionally, it only requires a single template without any training process. In this paper, a new method (MFDCM) for 3D multi-object pose estimation in a monocular image is proposed, which is based on the FDCM method with major performance improvements in accuracy and running time. These improvements were achieved by using the LSD method instead of a simple edge detector (Canny detector), using an angular Voronoi diagram instead of calculating the 3D distance transform image, a distance transform image, and an integral distance transform image at each orientation. In addition, the search process in the proposed method depends on a line segment-based search instead of the sliding window search in the FDCM. As a result, the proposed method is more robust and much faster than the FDCM method, and the position, scale, and rotation are invariant. In addition, the proposed method was evaluated and compared to different methods (COF, HALCON, LINE2D, and BOLD) using a D-textureless dataset. The comparison results show that the MFDCM has the highest score among all of the tested methods (with a slight advantage from the COF and BLOD methods) while it was a little slower than LINE2D (which was the fasted method among the compared methods). Furthermore, it was at least 14-times faster than the FDCM in the tested scenarios. The results prove that the MFDCM is able to detect and 3D pose estimate of object in a clear or clustered background from a monocular image with a high-speed running time, even if the objects are under partial occlusion; this makes it robust and reliable for real-time applications.
Źródło:
Computer Science; 2020, 21 (1); 97-112
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A single upper limb pose estimation method based on the improved stacked hourglass network
Autorzy:
Peng, Gang
Zheng, Yuezhi
Li, Jianfeng
Yang, Jin
Powiązania:
https://bibliotekanauki.pl/articles/1838179.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
convolutional neural network
stacked hourglass network
skeleton key point
single upper limb pose estimation
human machine coordination
Opis:
At present, most high-accuracy single-person pose estimation methods have high computational complexity and insufficient real-time performance due to the complex structure of the network model. However, a single-person pose estimation method with high real-time performance also needs to improve its accuracy due to the simple structure of the network model. It is currently difficult to achieve both high accuracy and real-time performance in single-person pose estimation. For use in human–machine cooperative operations, this paper proposes a single-person upper limb pose estimation method based on an end-to-end approach for accurate and real-time limb pose estimation. Using the stacked hourglass network model, a single-person upper limb skeleton key point detection model is designed. A deconvolution layer is employed to replace the up-sampling operation of the hourglass module in the original model, solving the problem of rough feature maps. Integral regression is used to calculate the position coordinates of key points of the skeleton, reducing quantization errors and calculations. Experiments show that the developed single-person upper limb skeleton key point detection model achieves high accuracy and that the pose estimation method based on the end-to-end approach provides high accuracy and real-time performance.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2021, 31, 1; 123-133
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Interdisciplinary collaboration the present and the future - the role of computer science in physiotherapy
Współpraca interdyscyplinarna teraźniejszością i przyszłością - rola informatyki w fizjoterapii
Autorzy:
Mikołajewska, Emilia
Mikołajewski, Dariusz
Powiązania:
https://bibliotekanauki.pl/articles/41203415.pdf
Data publikacji:
2024
Wydawca:
Uniwersytet Kazimierza Wielkiego w Bydgoszczy
Tematy:
physiotherapy
exercise evaluation
mobile health
pose estimation
rehabilitation
remote monitoring
fizjoterapia
ocena ćwiczeń
zdrowie mobilne
ocena pozycji
rehabilitacja
zdalny monitoring
Opis:
IT methods and tools are increasingly used in physiotherapy diagnosis and therapy, as well as in the care and monitoring of patients in telerehabilitation and home rehabilitation, and physiotherapy itself is becoming increasingly interdisciplinary. The aim of the article is to review to what extent the opportunities related to the interdisciplinary development of physiotherapy have been used and how much potential there is for further, stimulated development.
Metody i narzędzia informatyczne znajdują coraz szersze zastosowanie w diagnostyce i terapii fizjoterapeutycznej oraz opiece i monitorowaniu pacjentów w ramach telerehabilitacji i rehabilitacji domowej, a sama fizjoterapia staje się coraz bardziej interdyscyplinarna. Celem artykułu jest przegląd, na ile możliwości związane z interdyscyplinarnym rozwojem fizjoterapii zostały wykorzystane, a ile w nich tkwi jeszcze potencjału na dalszy, stymulowany rozwój.
Źródło:
Studia i Materiały Informatyki Stosowanej; 2024, 16, 2; 13-20
1689-6300
Pojawia się w:
Studia i Materiały Informatyki Stosowanej
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
LEDs based video camera pose estimation
Autorzy:
Sudars, K.
Cacurs, R.
Homjakovs, I.
Judvaitis, J.
Powiązania:
https://bibliotekanauki.pl/articles/200249.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
camera pose estimation
image keypoint detection and matching
3D point reconstruction
object localization and tracking
oszacowanie ustawienia kamery
rekonstrukcja modelu 3D
lokalizacja obiektu
śledzenie obiektu
Opis:
For 3D object localization and tracking with multiple cameras the camera poses have to be known within a high precision. The paper evaluates camera pose estimation via a fundamental matrix and via the known object in environment of multiple static cameras. A special feature point extraction technique based on LED (Light Emitting Diodes) point detection and matching has been developed for this purpose. LED point detection has been solved searching local maximums in images and LED point matching has been solved involving patterned time functions for each light source. Emitting LEDs have been used as sources of known reference points instead of typically used feature point extractors like ORB, SIFT, SURF etc. In such a way the robustness of pose estimation has been obtained. Camera pose estimation is significant for object localization using the networks with multiple cameras which are going to an play increasingly important role in modern Smart Cities environments.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2015, 63, 4; 897-905
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-7 z 7

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