Comparative Analysis of Skeleton-Based Human Pose Estimation

Chung, Jen-Li; Ong, Lee-Yeng; Leow, Meng-Chew

doi:10.3390/fi14120380

Cited by 57 publications

(25 citation statements)

References 41 publications

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“…Human pose estimation is the primary technology in human action recognition, and its accuracy directly affects action recognition performance. According to the different data modes, human pose estimation methods can be divided into four categories: appearancebased, depth-based, optical flow-based, and skeleton-based (Chung et al, 2022). Skeletonbased schemes have recently attracted more researchers' attention due to their high recognition rate, fineness, and robustness (Sipola et al, 2022).…”

Section: Human Pose Estimationmentioning

confidence: 99%

A Lightweight Multi-Person Pose Estimation Scheme Based on Jetson Nano

Liu¹,

Blancaflor

Abisado

2023

acs

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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.

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Section: Human Pose Estimationmentioning

confidence: 99%

A Lightweight Multi-Person Pose Estimation Scheme Based on Jetson Nano

Liu¹,

Blancaflor

Abisado

2023

acs

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“…Human pose estimation refers to determining the position of human joints (known as body key points) [26][27][28]. Key points extracted from pose estimation contain helpful information and details about the gestures and actions of the subject.…”

Section: Static Pose Features Static Pose Features Consist Of the Pos...mentioning

confidence: 99%

Video alignment using unsupervised learning of local and global features

Fakhfour,

ShahverdiKondori,

Mohammadzade

2023

Preprint

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In this paper, we tackle the problem of video alignment, the process of matching the frames of a pair of videos containing similar actions. The main challenge in video alignment is that accurate correspondence should be established despite the differences in the execution processes and appearances between the two videos. We introduce an unsupervised method for alignment that uses global and local features of the frames. In particular, we introduce effective features for each video frame using three machine vision tools: person detection, pose estimation, and VGG network. Then, the features are processed and combined to construct a multidimensional time series that represents the video. The resulting time series are used to align videos of the same actions using a novel version of dynamic time warping named Diagonalized Dynamic Time Warping(DDTW). The main advantage of our approach is that no training is required, which makes it applicable for any new type of action without any need to collect training samples for it. For evaluation, we considered video synchronization and phase classification tasks on the Penn action dataset \citep{penn}. Also, for an effective evaluation of the video synchronization task, we present a new metric called Enclosed Area Error(EAE). The results show that our method outperforms previous state-of-the-art methods, such as TCC \citep{TCC}, and other self-supervised and weakly supervised methods.

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“…The dataset includes 41 exercise names and different numbers of models, with 17 fullbody workouts (43%), 16 barbell/dumbbell exercises (24% barbell, 19% dumbbell), and 8 furniture-based exercises (15%). The five postures include standing (20), supine (6), prone (3), with bench (4), and with furniture (8). The label serial number is assigned to each image in the order of motion, with each exercise repeated 4-5 times within 32 frames.…”

Section: Dataset Preparation and Definition Of Each Exercisementioning

confidence: 99%

A Comparative Study of Automated Machine Learning Platforms for Exercise Anthropometry-Based Typology Analysis: Performance Evaluation of AWS SageMaker, GCP VertexAI, and MS Azure

Choi

Choi²,

Heo

2023

Bioengineering

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The increasing prevalence of machine learning (ML) and automated machine learning (AutoML) applications across diverse industries necessitates rigorous comparative evaluations of their predictive accuracies under various computational environments. The purpose of this research was to compare and analyze the predictive accuracy of several machine learning algorithms, including RNNs, LSTMs, GRUs, XGBoost, and LightGBM, when implemented on different platforms such as Google Colab Pro, AWS SageMaker, GCP Vertex AI, and MS Azure. The predictive performance of each model within its respective environment was assessed using performance metrics such as accuracy, precision, recall, F1-score, and log loss. All algorithms were trained on the same dataset and implemented on their specified platforms to ensure consistent comparisons. The dataset used in this study comprised fitness images, encompassing 41 exercise types and totaling 6 million samples. These images were acquired from AI-hub, and joint coordinate values (x, y, z) were extracted utilizing the Mediapipe library. The extracted values were then stored in a CSV format. Among the ML algorithms, LSTM demonstrated the highest performance, achieving an accuracy of 73.75%, precision of 74.55%, recall of 73.68%, F1-score of 73.11%, and a log loss of 0.71. Conversely, among the AutoML algorithms, XGBoost performed exceptionally well on AWS SageMaker, boasting an accuracy of 99.6%, precision of 99.8%, recall of 99.2%, F1-score of 99.5%, and a log loss of 0.014. On the other hand, LightGBM exhibited the poorest performance on MS Azure, achieving an accuracy of 84.2%, precision of 82.2%, recall of 81.8%, F1-score of 81.5%, and a log loss of 1.176. The unnamed algorithm implemented on GCP Vertex AI showcased relatively favorable results, with an accuracy of 89.9%, precision of 94.2%, recall of 88.4%, F1-score of 91.2%, and a log loss of 0.268. Despite LightGBM’s lackluster performance on MS Azure, the GRU implemented in Google Colab Pro displayed encouraging results, yielding an accuracy of 88.2%, precision of 88.5%, recall of 88.1%, F1-score of 88.4%, and a log loss of 0.44. Overall, this study revealed significant variations in performance across different algorithms and platforms. Particularly, AWS SageMaker’s implementation of XGBoost outperformed other configurations, highlighting the importance of carefully considering the choice of algorithm and computational environment in predictive tasks. To gain a comprehensive understanding of the factors contributing to these performance discrepancies, further investigations are recommended.

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Comparative Analysis of Skeleton-Based Human Pose Estimation

Cited by 57 publications

References 41 publications

A Lightweight Multi-Person Pose Estimation Scheme Based on Jetson Nano

A Lightweight Multi-Person Pose Estimation Scheme Based on Jetson Nano

Video alignment using unsupervised learning of local and global features

A Comparative Study of Automated Machine Learning Platforms for Exercise Anthropometry-Based Typology Analysis: Performance Evaluation of AWS SageMaker, GCP VertexAI, and MS Azure

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