Temat: human pose estimation

Skocz do pozycji: 1.

Tytuł:: A lightweight multi-person pose estimation scheme based on Jetson Nano
Autorzy:: Liu, Lei
Blancaflor, Eric B.
Abisado, Mideth
Tematy:: human pose estimation
lightweight model
Edge AI
deep learning
computer vision; Pokaż więcej
Wydawca:: Polskie Towarzystwo Promocji Wiedzy
Powiązania:: https://bibliotekanauki.pl/articles/30148243.pdf Link otwiera się w nowym oknie
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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

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
Tematy:: YOLOv5
HRNet
2D key points estimation
2D human pose estimation; Pokaż więcej
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Powiązania:: https://bibliotekanauki.pl/articles/2147147.pdf Link otwiera się w nowym oknie
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.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Utilization of computer vision in Video Assistant Referee for Powerlifting
Wykorzystanie wizji komputerowej w systemie VAR dla trójboju siłowego
Autorzy:: Nowogórski, Dominik
Opis:: Computer vision is a dynamically developing field of computer science. This work proposes a Video Assistant Referee system for powerlifting, utilizing the most recent solutions to problems of object tracking and human pose estimation by key points detection. The challenges faced by referees in the sport and ways of aiding their job with new technologies are discussed. Implemented functionalities are wrapped in a graphical interface that allows usage of the system during a competition. Effectiveness of presented tools and algorithms is verified with video material extracted from a full live stream of world powerlifting championships.
Wizja komputerowa jest prężnie rozwijającą się dziedziną informatyki. W niniejszej pracy zaproponowano system VAR dla trójboju siłowego, korzystający z najbardziej aktualnych rozwiązań problemów śledzenia ruchu obiektów oraz estymacji pozy człowieka poprzez detekcję punktów kluczowych na jego ciele. Przedstawiono wyzwania stojące przed arbitrami tej dyscypliny sportowej i sposoby wspomagania sędziowania przy użyciu nowych technologii. Całość funkcjonalności została obudowana w interfejs graficzny, umożliwiający obsługę systemu w warunkach trwających zawodów sportowych. Skuteczność zaprezentowanych narzędzi i algorytmów zweryfikowano przy pomocy materiałów wideo pochodzących z pełnej transmisji mistrzostw świata w trójboju siłowym.
Dostawca treści:: Repozytorium Uniwersytetu Jagiellońskiego

Inne

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Skocz do pozycji: 4.

Tytuł:: A single upper limb pose estimation method based on the improved stacked hourglass network
Autorzy:: Peng, Gang
Zheng, Yuezhi
Li, Jianfeng
Yang, Jin
Tematy:: convolutional neural network
stacked hourglass network
skeleton key point
single upper limb pose estimation
human machine coordination; Pokaż więcej
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Powiązania:: https://bibliotekanauki.pl/articles/1838179.pdf Link otwiera się w nowym oknie
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.
Dostawca treści:: Biblioteka Nauki

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