Deep 3D Human Pose Estimation Under Partial Body Presence

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ISBN 13 :
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Book Rating : 4.:/5 (129 download)

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Book Synopsis Deep 3D Human Pose Estimation Under Partial Body Presence by : Saeid Vosoughi

Download or read book Deep 3D Human Pose Estimation Under Partial Body Presence written by Saeid Vosoughi and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 3D human pose estimation is estimating the position of the main body joints in the 3D space from 2D images. It remains a challenging problem despite being well studied in computer vision domain. This stems from the ambiguity caused by capturing 2D imagery from 3D objects and thus the loss of depth information. 3D human pose estimation is especially challenging when not all the human body is present (visible) in the input 2D image. This work proposes solutions to reconstruct the 3D human pose from a 2D image under partial body presence. Partial body presence includes all the cases in which some of the body's main joints do not fall inside the image. We propose two different deep learning based approaches to address partial body presence: 1) 3D pose estimation from 2D poses estimated from the 2D input image and 2) 3D pose estimation directly from the 2D input image. In both approaches, we use Convolutional Neural Networks (CNN) for regression. These networks are designed and trained to work under partial body presence but output the full 3D human pose (i.e., including not visible joints). In addition, we propose a detection CNN network to detect those joints present in the input image. We then propose to integrate both regression and detection networks so to estimate the partial 3D human pose, in addition to the full 3D human pose estimated by the regression network. Experimental results comparing the performance of the state-of-the-art demonstrate the effectiveness of our approaches under partial body presence. Experimental results also show that the direct regression of the 3D human pose from 2D images yields more accurate estimation compared to having 2D pose estimation as an intermediate stage.

Pattern Recognition and Computer Vision

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Publisher : Springer Nature
ISBN 13 : 9819984327
Total Pages : 518 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Pattern Recognition and Computer Vision by : Qingshan Liu

Download or read book Pattern Recognition and Computer Vision written by Qingshan Liu and published by Springer Nature. This book was released on 2023-12-23 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis.

Conditional Models for 3D Human Pose Estimation

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ISBN 13 :
Total Pages : 195 pages
Book Rating : 4.:/5 (67 download)

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Book Synopsis Conditional Models for 3D Human Pose Estimation by : Atul Kanaujia

Download or read book Conditional Models for 3D Human Pose Estimation written by Atul Kanaujia and published by . This book was released on 2010 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: Human 3d pose estimation from monocular sequence is a challenging problem, owing to highly articulated structure of human body, varied anthropometry, self occlusion, depth ambiguities and large variability in the appearance and background in which humans may appear. Conventional vision based approaches to human 3d pose estimation mostly employed "top-down methods", which used a complete 3d human model, in a hypothesized pose, to explain the configuration of the humans in the observed 2d image. In this thesis, we work with "bottom-up methods" for human pose estimation, that use low level image features to directly predict 3d pose. The research draws on recent innovations in statistical learning, observation-driven modeling, stable image encodings, semi-supervised learning and learning perceptual representations. We address the problems of (a) modeling pose ambiguities due to 3d-to-2d projection and self occlusion, (b) lack of sufficient labeled data for training discriminative models and (c) high dimensionality of human 3d pose state space. In order to resolve 3d pose ambiguities, we use multi-valued functions to predict multiple plausible 3d poses for an image observation. We incorporate unlabeled data in a semi-supervised learning framework to constrain and improve the training of discriminative models. We also propose generic probabilistic Spectral Latent Variable Models to efficiently learn low dimensional representations of high dimensional observation data and apply it to the problem of human 3d pose inference.

A Human Kinetic Dataset and a Hybrid Model for 3D Human Pose Estimation

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ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (129 download)

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Book Synopsis A Human Kinetic Dataset and a Hybrid Model for 3D Human Pose Estimation by : Jianquan Wang

Download or read book A Human Kinetic Dataset and a Hybrid Model for 3D Human Pose Estimation written by Jianquan Wang and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Human pose estimation represents the skeleton of a person in color or depth images to improve a machine's understanding of human movement. 3D human pose estimation uses a three-dimensional skeleton to represent the human body posture, which is more stereoscopic than a two-dimensional skeleton. Therefore, 3D human pose estimation can enable machines to play a role in physical education and health recovery, reducing labor costs and the risk of disease transmission. However, the existing datasets for 3D pose estimation do not involve fast motions that would cause optical blur for a monocular camera but would allow the subjects' limbs to move in a more extensive range of angles. The existing models cannot guarantee both real-time performance and high accuracy, which are essential in physical education and health recovery applications. To improve real-time performance, researchers have tried to minimize the size of the model and have studied more efficient deployment methods. To improve accuracy, researchers have tried to use heat maps or point clouds to represent features, but this increases the difficulty of model deployment. To address the lack of datasets that include fast movements and easy-to-deploy models, we present a human kinetic dataset called the Kivi dataset and a hybrid model that combines the benefits of a heat map-based model and an end-to-end model for 3D human pose estimation. We describe the process of data collection and cleaning in this thesis. Our proposed Kivi dataset contains large-scale movements of humans. In the dataset, 18 joint points represent the human skeleton. We collected data from 12 people, and each person performed 38 sets of actions. Therefore, each frame of data has a corresponding person and action label. We design a preliminary model and propose an improved model to infer 3D human poses in real time. When validating our method on the Invariant Top-View (ITOP) dataset, we found that compared with the initial model, our improved model improves the mAP@10cm by 29%. When testing on the Kivi dataset, our improved model improves the mAP@10cm by 15.74% compared to the preliminary model. Our improved model can reach 65.89 frames per second (FPS) on the TensorRT platform.

Human Pose Estimation with Implicit Shape Models

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Publisher : KIT Scientific Publishing
ISBN 13 : 3731501848
Total Pages : 293 pages
Book Rating : 4.7/5 (315 download)

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Book Synopsis Human Pose Estimation with Implicit Shape Models by : Brauer, Juergen

Download or read book Human Pose Estimation with Implicit Shape Models written by Brauer, Juergen and published by KIT Scientific Publishing. This book was released on 2014-04-29 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work presents a new approach for estimating 3D human poses based on monocular camera information only. For this, the Implicit Shape Model is augmented by new voting strategies that allow to localize 2D anatomical landmarks in the image. The actual 3D pose estimation is then formulated as a Particle Swarm Optimization (PSO) where projected 3D pose hypotheses are compared with the generated landmark vote distributions.

Learning to Estimate 3D Human Pose from Point Cloud

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ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (129 download)

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Book Synopsis Learning to Estimate 3D Human Pose from Point Cloud by : Yufan Zhou

Download or read book Learning to Estimate 3D Human Pose from Point Cloud written by Yufan Zhou and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: As the development of the health and well-being industry advances, the importance of maintaining physical exercise on a regular basis should not be understated. To help people evaluate their pose during exercise, pose estimation has aroused massive interest among researchers from various fields. Meanwhile, pose estimation, especially 3D pose estimation, is a challenging problem in computer vision. Although substantial progress has been made over the past few years, there are still some limitations, such as low accuracy and the lack of comprehensive and challenging datasets for use and comparison. In this thesis, we study the task of 3D human pose estimation from depth images. Different from the existing CNN-based human pose estimation method, we propose a deep human pose network for 3D pose estimation by taking the point cloud data as input data to model the surface of complex human structures. We first cast the 3D human pose estimation from 2.5D depth images to 3D point clouds and directly predict the 3D joint positions. Our proposed methodology combining a two-stage training strategy is crucial for pose estimation tasks. The experiments on two public datasets show that our approach achieves higher accuracy than previous state-of-art methods. Our method reaches an accuracy of 85.11% and 78.46% on both parts of the ITOP dataset and an accuracy of 80.86% on the EVAL dataset.

3D Human Pose Estimation on Taiji Sequence

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ISBN 13 :
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Book Synopsis 3D Human Pose Estimation on Taiji Sequence by : Tianhe Wang

Download or read book 3D Human Pose Estimation on Taiji Sequence written by Tianhe Wang and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Human pose estimation is a task that has been extensively studied in the field of computer vision. Given a video frame or an image, a 2D pose or 3D pose estimation can be generated directly. An alternative to 3D pose estimation is to estimate a 2D human pose first and then predict the 3D pose from 2D joint locations. In our experiments, we have found that the state-of-the-art 3D pose estimators have over 60 mm MPJPE (mean per joint position error), it is unacceptable for biomedical applications where expected errors are 1% or less of the height of the person (e.g. 17mm for a person with 1.7m tall). To be able to achieve the precision expected in the biomedical applications, training on a biomedically validated dataset is a start.The goal of this thesis is to achieve quantified initial results of a 3D pose estimator trained on biomedically validated Taiji Quan sequence dataset. This thesis contains three parts: (1) A tool designed to align MoCap data with video temporally; (2)A network trained to estimate 3D human pose from the 2D skeleton on the Taiji dataset. The 2D skeleton is generated by projecting, randomly, MoCap data into multiple 2D planes. (3) With the aligned video-MoCap data and OpenPose as a 2D human joint detector, a 3D human pose estimator was implemented. The network from (2) was fine-tuned to work on the noisy 2D joint results from video frames. As a result, The 3D skeleton reconstruction from the 2D skeletons of two different views by triangulation achieves the least MPJPE 0.7cm; the 3D pose estimation from video achieves around 26cm MPJPE and one of the state-of-the-art 3D pose estimators (Lifting from the deep) achieves around 43cm MPJPE. As a conclusion, Dual-view outperforms single-view significantly as expected because depth information of 3D skeleton can be obtained from the Dual-view. For single-views, the network trained on biomedically validated Taiji dataset outperforms "Lifting from the deep." But there is still a long way to go to meet the expected errors for biomedical applications for single-view pose estimators.

Human Pose Estimation With Implicit Shape Models

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Publisher :
ISBN 13 : 9781013280429
Total Pages : 290 pages
Book Rating : 4.2/5 (84 download)

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Book Synopsis Human Pose Estimation With Implicit Shape Models by : Jürgen Brauer

Download or read book Human Pose Estimation With Implicit Shape Models written by Jürgen Brauer and published by . This book was released on 2020-10-09 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work presents a new approach for estimating 3D human poses based on monocular camera information only. For this, the Implicit Shape Model is augmented by new voting strategies that allow to localize 2D anatomical landmarks in the image. The actual 3D pose estimation is then formulated as a Particle Swarm Optimization (PSO) where projected 3D pose hypotheses are compared with the generated landmark vote distributions. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.

Deep Learning Based Method for 3d Human Pose Estimation from 2d Fisheye Images

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ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (15 download)

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Book Synopsis Deep Learning Based Method for 3d Human Pose Estimation from 2d Fisheye Images by : 陳靖淳

Download or read book Deep Learning Based Method for 3d Human Pose Estimation from 2d Fisheye Images written by 陳靖淳 and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Towards Multi-person 3D Pose Estimation in Natural Videos

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ISBN 13 :
Total Pages : 71 pages
Book Rating : 4.:/5 (119 download)

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Book Synopsis Towards Multi-person 3D Pose Estimation in Natural Videos by : Renshu GU

Download or read book Towards Multi-person 3D Pose Estimation in Natural Videos written by Renshu GU and published by . This book was released on 2020 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: Despite the increasing need of analyzing human poses on the street and in the wild, multi-person 3D pose estimation using static or moving monocular camera in real-world scenarios remains a challenge, requiring large-scale training data or high computation complexity due to the high degrees of freedom in 3D human poses. To address these challenges, a novel scheme, Hierarchical 3D Human Pose Estimation (H3DHPE), is proposed to effectively track and hierarchically estimate 3D human poses in natural videos in an efficient fashion. Torso estimation is formulated as a Perspective-N-Point (PNP) problem, limb pose estimation is solved as an optimization problem, and the high dimensional pose estimation is hierarchically addressed efficiently. As an extension to Hierarchical 3D Human Pose Estimation (H3DHPE), Universal Hierarchical 3D Human Pose Estimation (UH3DHPE) is proposed to handle the case of an occluded or inaccurate 2D torso keypoints, which makes torso-first estimation in H3DHPE unreliable. An effective method to directly estimate limb poses without building upon the estimated torso pose is proposed, and the torso pose can then be further refined to form the hierarchy in a bottom-up fashion. An adaptive merging strategy is proposed to determine the best hierarchy. The advantages of the proposed unsupervised methods are validated on various datasets including a lot of natural real-world scenes. For better evaluation and future research, a unique dataset called Moving camera Multi-Human interactions (MMHuman) is collected, with accurate MoCap ground truth, for multi-person interaction scenarios recorded by a monocular moving camera. Superior performance is shown on the newly collected MMHuman compared to state-of-the-art methods, including supervised methods, proving that our unsupervised solution generalize better to natural videos. To further tackle the problem of long term occlusions, a deep neutral network (DNN) solution is explored for trajectory recovery. To our best knowledge, it’s the first to use temporal gated convolutions to recover missing poses and address the occlusion issues in the pose estimation. A simple yet effective approach is proposed to transform normalized poses to the global trajectory into the camera coordinate.

3D Front-view Human Upper Body Pose Estimation Using Single Camera

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ISBN 13 :
Total Pages : 88 pages
Book Rating : 4.:/5 (881 download)

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Book Synopsis 3D Front-view Human Upper Body Pose Estimation Using Single Camera by : Ruizhi Sun

Download or read book 3D Front-view Human Upper Body Pose Estimation Using Single Camera written by Ruizhi Sun and published by . This book was released on 2013 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt: 3D human pose estimation is an important field in Computer Vision. It has a wide range of applications, such as human-computer interaction, intelligent animation synthesis, video surveillance, etc. Single camera video, due to the lack of depth information, causes difficult challenges of estimating 3D human pose. This paper proposes a modified particle swarm optimization method combined with human motion prior knowledge in order to achieve a robust analysis-via-synthesis strategy. Due to the numerous applications of human upper body movements, we are focusing on creating a front-view human upper body model. Due to the high dimensional body configuration of human pose estimation, particle swarm optimization, with great global search ability, has a very slow convergence speed. Therefore, our modified algorithm uses annealing method so that the particles can converge faster to the lowest likelihood function value. This fact makes our algorithm more effective. Integrated use of several image features, such as silhouette, arm silhouette, ratio silhouette area, edge, motion and skin color, constructs our cost function. Each feature has its unique purpose in order to achieve much more accurate and robust pose estimation results. Constraining human body configuration, including the perspective scope of joint movements angle range constraints and non-penetrating constraints of limbs, is to make sure estimating human pose in the feasible region, preventing illegal pose data, and improve the accuracy of 3D human tracking. In addition, a trajectory feature is used to re-distribute particles for every frame tracking. Experiment results show that our modified algorithm combined with cost function provides a much more accurate and robust result than downhill simplex algorithm [1] and Annealing Particle Swarm Optimization Particle Filter [2].

Human Pose Analysis

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Publisher : Springer
ISBN 13 : 9789819793334
Total Pages : 0 pages
Book Rating : 4.7/5 (933 download)

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Book Synopsis Human Pose Analysis by : Songlin Du

Download or read book Human Pose Analysis written by Songlin Du and published by Springer. This book was released on 2024-12-29 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Learning Robust Features and Latent Representations for Single View 3D Pose Estimation of Humans and Objects

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Publisher :
ISBN 13 :
Total Pages : 125 pages
Book Rating : 4.:/5 (15 download)

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Book Synopsis Learning Robust Features and Latent Representations for Single View 3D Pose Estimation of Humans and Objects by : Bugra Tekin

Download or read book Learning Robust Features and Latent Representations for Single View 3D Pose Estimation of Humans and Objects written by Bugra Tekin and published by . This book was released on 2018 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mots-clés de l'auteur: 3D human pose estimation ; 3D object pose estimation ; 6D pose estimation ; 3D computer vision ; motion compensation ; deep learning ; structured prediction.

Pose Estimation and Object Detection Using Deep Convolutional Networks

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ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (139 download)

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Book Synopsis Pose Estimation and Object Detection Using Deep Convolutional Networks by : Jianning Quan

Download or read book Pose Estimation and Object Detection Using Deep Convolutional Networks written by Jianning Quan and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Human pose estimation and object detection are fundamental problems in computer vision and autonomous systems with applications ranging from healthcare and sports to surveillance, autonomous driving and traffic monitoring. The task of 3D human pose estimation is to predict the positions of a person's joints, while the goal of object detection is to identify the object category and locate the position using a bounding box for every known object within an image or video. The contributions in this thesis are two-fold. One is to tackle the 3D human pose estimation problem in the graph-theoretic setting. More specifically, we introduce a higher-order graph convolutional framework with initial residual connections for 3D-to-2D pose estimation. The proposed approach is derived from implicit fairing on graphs using a scale-dependent graph Laplacian filtering scheme. Using multi-hop neighborhoods for node feature aggregation, our model is able to capture the long-range dependencies between body joints. Moreover, our approach alleviates the oversmoothing problem caused by repeated graph convolutions, preventing the learned feature representations from converging to similar values thanks in part to residual connections with the first layer of the network. These residual connections are integrated by design in our network architecture, and help ensure that the learned feature representations retain important information from the initial features of the input layer as the network depth increases. Experiments and ablations studies conducted on a standard benchmark demonstrate the effectiveness of our model, achieving superior performance over strong baseline methods for 3D human pose estimation. The other contribution consists of designing a single-stage object detection model for aerial imagery using a class-balanced loss function in conjunction with a feature pyramid network in an effort to mitigate the data imbalance problem without the need to rely on data augmentation. The key benefit of using the class-balanced focal loss is the ability to adjust the contributions of minority classes to the loss function with the aim to tackle the class imbalance problem, allowing our model to detect different classes evenly. The performance of our proposed object detection model is demonstrated through extensive experiments on a standard aerial image benchmark, achieving comparable or better object detection results in comparison with competing baselines.

Graph-based Deep Convolution Network for 3D Human Pose Estimation

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ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (122 download)

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Book Synopsis Graph-based Deep Convolution Network for 3D Human Pose Estimation by : Han-Kuang LI

Download or read book Graph-based Deep Convolution Network for 3D Human Pose Estimation written by Han-Kuang LI and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Proceedings of Second International Conference on Computing, Communications, and Cyber-Security

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Publisher : Springer Nature
ISBN 13 : 9811607338
Total Pages : 1027 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Proceedings of Second International Conference on Computing, Communications, and Cyber-Security by : Pradeep Kumar Singh

Download or read book Proceedings of Second International Conference on Computing, Communications, and Cyber-Security written by Pradeep Kumar Singh and published by Springer Nature. This book was released on 2021-05-24 with total page 1027 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features selected research papers presented at the Second International Conference on Computing, Communications, and Cyber-Security (IC4S 2020), organized in Krishna Engineering College (KEC), Ghaziabad, India, along with Academic Associates; Southern Federal University, Russia; IAC Educational, India; and ITS Mohan Nagar, Ghaziabad, India during 3–4 October 2020. It includes innovative work from researchers, leading innovators, and professionals in the area of communication and network technologies, advanced computing technologies, data analytics and intelligent learning, the latest electrical and electronics trends, and security and privacy issues.

Multi-view Part-based Models for 3D Human Pose Estimation in Real-World Scenes

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ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (18 download)

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Book Synopsis Multi-view Part-based Models for 3D Human Pose Estimation in Real-World Scenes by : Sikandar Amin

Download or read book Multi-view Part-based Models for 3D Human Pose Estimation in Real-World Scenes written by Sikandar Amin and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: