Cluster-wise learning network for multi-person pose estimation

Zhao, Ying; Luo, Zhiwei; Quan, Changqin; Liu, Dianchao; Wang, Gang

doi:10.1016/j.patcog.2019.107074

Cited by 14 publications

(3 citation statements)

References 48 publications

(81 reference statements)

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“…One of the key features of MediaPipe is its support for hardware acceleration, enabling efficient execution of computationally intensive tasks on various platforms, including CPUs, GPUs, and specialized hardware like Google's Edge TPU [24]. This ensures optimal performance and scalability across a wide range of devices, from mobile phones and laptops to edge devices and IoT (Internet of Things) devices.…”

Section: Mediapipementioning

confidence: 99%

Advancing Yoga Practice : Leveraging Opencv and Mediapipe for Pose Detection

2024

IRJMETS

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Yoga is a centuries-old practice that has become more popular all over the world due to its benefits for the body, mind, and spirit [1]. Nowadays, yoga is a popular kind of fitness all around the world. Proper posture is the cornerstone of yoga. Despite all of yoga's health benefits, bad posture can cause problems including sprains and soreness in the muscles. Over the past few years, working remotely has drawn more interest from people than working in person. Our approach is beneficial to people who are used to living online and struggle to find the time to visit yoga facilities. An increasing number of people are interested in automating the analysis of yoga postures in order to improve practitioners' performance and knowledge of the poses as a result of technological integration. Our technique is used by the suggested system to accurately extract features and estimate pose from input photos or video frames. The model classifies the yoga positions using our system's web cameras, and the image is used as input. The asana is improved by enhancing the input derived from the yoga postures through the application of multiple deep learning models.

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Section: Mediapipementioning

confidence: 99%

Advancing Yoga Practice : Leveraging Opencv and Mediapipe for Pose Detection

2024

IRJMETS

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“…However, they followed a bottom-up approach instead of a top-down one. Zhao et al [146] presented a network architecture consisting of a multistage and two branches that combined local and global features by combining dense and sparse keypoint clusters in each branch to detect key points. Additionally, intra-and interclusters were used for grouping predicted key points into individuals.…”

Section: Bottom-up Approachmentioning

confidence: 99%

“…Huge datasets like ImageNet train these models. Pretrained models such as Hourglass, ResNet, HRNet, and HrHRNet are the most used by different studies [137,143,146]. Each of these models attempts to solve specific problems.…”

Section: What Are the Pretrained Models Used For Extracting The Featu...mentioning

confidence: 99%

Human Pose Estimation Using Deep Learning: A Systematic Literature Review

Samkari,

Arif,

Alghamdi

et al. 2023

MAKE

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Human Pose Estimation (HPE) is the task that aims to predict the location of human joints from images and videos. This task is used in many applications, such as sports analysis and surveillance systems. Recently, several studies have embraced deep learning to enhance the performance of HPE tasks. However, building an efficient HPE model is difficult; many challenges, like crowded scenes and occlusion, must be handled. This paper followed a systematic procedure to review different HPE models comprehensively. About 100 articles published since 2014 on HPE using deep learning were selected using several selection criteria. Both image and video data types of methods were investigated. Furthermore, both single and multiple HPE methods were reviewed. In addition, the available datasets, different loss functions used in HPE, and pretrained feature extraction models were all covered. Our analysis revealed that Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are the most used in HPE. Moreover, occlusion and crowd scenes remain the main problems affecting models’ performance. Therefore, the paper presented various solutions to address these issues. Finally, this paper highlighted the potential opportunities for future work in this task.

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Multi-object behaviour recognition based on object detection cascaded image classification in classroom scenes

Dang,

Liu,

et al. 2024

Appl Intell

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Cluster-wise learning network for multi-person pose estimation

Cited by 14 publications

References 48 publications

Advancing Yoga Practice : Leveraging Opencv and Mediapipe for Pose Detection

Advancing Yoga Practice : Leveraging Opencv and Mediapipe for Pose Detection

Human Pose Estimation Using Deep Learning: A Systematic Literature Review

Multi-object behaviour recognition based on object detection cascaded image classification in classroom scenes

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