scholarly journals EfficientPose: Scalable single-person pose estimation

2020 ◽  
Author(s):  
Daniel Groos ◽  
Heri Ramampiaro ◽  
Espen AF Ihlen
Keyword(s):  
Pose Estimation ◽  
Network Architecture ◽  
State Of The Art ◽  
Computational Cost ◽  
Real Life ◽  
Low Complexity ◽  
Human Pose Estimation ◽  
Efficient Detection ◽  
Single Person ◽  
Human Pose

Abstract Single-person human pose estimation facilitates markerless movement analysis in sports, as well as in clinical applications. Still, state-of-the-art models for human pose estimation generally do not meet the requirements of real-life applications. The proliferation of deep learning techniques has resulted in the development of many advanced approaches. However, with the progresses in the field, more complex and inefficient models have also been introduced, which have caused tremendous increases in computational demands. To cope with these complexity and inefficiency challenges, we propose a novel convolutional neural network architecture, called EfficientPose, which exploits recently proposed EfficientNets in order to deliver efficient and scalable single-person pose estimation. EfficientPose is a family of models harnessing an effective multi-scale feature extractor and computationally efficient detection blocks using mobile inverted bottleneck convolutions, while at the same time ensuring that the precision of the pose configurations is still improved. Due to its low complexity and efficiency, EfficientPose enables real-world applications on edge devices by limiting the memory footprint and computational cost. The results from our experiments, using the challenging MPII single-person benchmark, show that the proposed EfficientPose models substantially outperform the widely-used OpenPose model both in terms of accuracy and computational efficiency. In particular, our top-performing model achieves state-of-the-art accuracy on single-person MPII, with low-complexity ConvNets.

scholarly journals Designing Compact Convolutional Filters for Lightweight Human Pose Estimation

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shili Niu ◽  
Weihua Ou ◽  
Shihua Feng ◽  
Jianping Gou ◽  
Fei Long ◽  
...  
Keyword(s):  
Pose Estimation ◽  
State Of The Art ◽  
Computational Cost ◽  
Estimation Accuracy ◽  
Human Pose Estimation ◽  
Model Parameters ◽  
Resource Limited ◽  
Benchmark Datasets ◽  
Human Pose ◽  
Low Computational Cost

Existing methods for human pose estimation usually use a large intermediate tensor, leading to a high computational load, which is detrimental to resource-limited devices. To solve this problem, we propose a low computational cost pose estimation network, MobilePoseNet, which includes encoder, decoder, and parallel nonmaximum suppression operation. Specifically, we design a lightweight upsampling block instead of transposing the convolution as the decoder and use the lightweight network as our downsampling part. Then, we choose the high-resolution features as the input for upsampling to reduce the number of model parameters. Finally, we propose a parallel OKS-NMS, which significantly outperforms the conventional NMS in terms of accuracy and speed. Experimental results on the benchmark datasets show that MobilePoseNet obtains almost comparable results to state-of-the-art methods with a low compilation load. Compared to SimpleBaseline, the parameter of MobilePoseNet is only 4%, while the estimation accuracy reaches 98%.

scholarly journals MEMe: A Mutually Enhanced Modeling Method for Efficient and Effective Human Pose Estimation

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 632
Author(s):  
Jie Li ◽  
Zhixing Wang ◽  
Bo Qi ◽  
Jianlin Zhang ◽  
Hu Yang
Keyword(s):  
Pose Estimation ◽  
State Of The Art ◽  
Model Performance ◽  
Low Complexity ◽  
Modeling Method ◽  
Human Pose Estimation ◽  
Baseline Model ◽  
Performance Gap ◽  
Human Pose ◽  
Total Fusion

In this paper, a mutually enhanced modeling method (MEMe) is presented for human pose estimation, which focuses on enhancing lightweight model performance, but with low complexity. To obtain higher accuracy, a traditional model scale is largely expanded with heavy deployment difficulties. However, for a more lightweight model, there is a large performance gap compared to the former; thus, an urgent need for a way to fill it. Therefore, we propose a MEMe to reconstruct a lightweight baseline model, EffBase transferred intuitively from EfficientDet, into the efficient and effective pose (EEffPose) net, which contains three mutually enhanced modules: the Enhanced EffNet (EEffNet) backbone, the total fusion neck (TFNeck), and the final attention head (FAHead). Extensive experiments on COCO and MPII benchmarks show that our MEMe-based models reach state-of-the-art performances, with limited parameters. Specifically, in the same conditions, our EEffPose-P0 with 256 × 192 can use only 8.98 M parameters to achieve 75.4 AP on the COCO val set, which outperforms HRNet-W48, but with only 14% of its parameters.

scholarly journals Lightweight Stacked Hourglass Network for Human Pose Estimation

2020 ◽  
Vol 10 (18) ◽  
pp. 6497
Author(s):  
Seung-Taek Kim ◽  
Hyo Jong Lee
Keyword(s):  
Receptive Field ◽  
Pose Estimation ◽  
Network Architecture ◽  
Human Pose Estimation ◽  
Current State ◽  
Reduction Methods ◽  
Substantial Progress ◽  
Human Pose ◽  
Residual Block ◽  
Point Detection

Human pose estimation is a problem that continues to be one of the greatest challenges in the field of computer vision. While the stacked structure of an hourglass network has enabled substantial progress in human pose estimation and key-point detection areas, it is largely used as a backbone network. However, it also requires a relatively large number of parameters and high computational capacity due to the characteristics of its stacked structure. Accordingly, the present work proposes a more lightweight version of the hourglass network, which also improves the human pose estimation performance. The new hourglass network architecture utilizes several additional skip connections, which improve performance with minimal modifications while still maintaining the number of parameters in the network. Additionally, the size of the convolutional receptive field has a decisive effect in learning to detect features of the full human body. Therefore, we propose a multidilated light residual block, which expands the convolutional receptive field while also reducing the computational load. The proposed residual block is also invariant in scale when using multiple dilations. The well-known MPII and LSP human pose datasets were used to evaluate the performance using the proposed method. A variety of experiments were conducted that confirm that our method is more efficient compared to current state-of-the-art hourglass weight-reduction methods.

Learning human poses in natural scenes

2018 ◽  
Author(s):  
◽  
Guanghan Ning
Keyword(s):  
Computer Vision ◽  
Pose Estimation ◽  
The Body ◽  
Human Pose Estimation ◽  
Natural Scenes ◽  
Top Down ◽  
University Of Missouri ◽  
Single Person ◽  
Human Pose ◽  
High Level

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The task of human pose estimation in natural scenes is to determine the precise pixel locations of body keypoints. It is very important for many high-level computer vision tasks, including action and activity recognition, human-computer interaction, motion capture, and animation. We cover two different approaches for this task: top-down approach and bottom-up approach. In the top-down approach, we propose a human tracking method called ROLO that localizes each person. We then propose a state-of-the-art single-person human pose estimator that predicts the body keypoints of each individual. In the bottomup approach, we propose an efficient multi-person pose estimator with which we participated in a PoseTrack challenge [11]. On top of these, we propose to employ adversarial training to further boost the performance of single-person human pose estimator while generating synthetic images. We also propose a novel PoSeg network that jointly estimates the multi-person human poses and semantically segment the portraits of these persons at pixel-level. Lastly, we extend some of the proposed methods on human pose estimation and portrait segmentation to the task of human parsing, a more finegrained computer vision perception of humans.

DSPNet: A low computational-cost network for human pose estimation

2021 ◽  
Vol 423 ◽  
pp. 327-335
Author(s):  
Fujin Zhong ◽  
Mingyang Li ◽  
Kun Zhang ◽  
Jun Hu ◽  
Li Liu
Keyword(s):  
Pose Estimation ◽  
Computational Cost ◽  
Human Pose Estimation ◽  
Human Pose ◽  
Low Computational Cost

Recent Advances in 3D Human Pose Estimation: From Optimization to Implementation and Beyond

2022 ◽  
Author(s):  
Jielu Yan ◽  
MingLiang Zhou ◽  
Jinli Pan ◽  
Meng Yin ◽  
Bin Fang
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Recent Progress ◽  
State Of The Art ◽  
The State ◽  
Human Pose Estimation ◽  
Estimation Techniques ◽  
Comprehensive Survey ◽  
Human Pose ◽  
3D Human Pose Estimation

3D human pose estimation describes estimating 3D articulation structure of a person from an image or a video. The technology has massive potential because it can enable tracking people and analyzing motion in real time. Recently, much research has been conducted to optimize human pose estimation, but few works have focused on reviewing 3D human pose estimation. In this paper, we offer a comprehensive survey of the state-of-the-art methods for 3D human pose estimation, referred to as pose estimation solutions, implementations on images or videos that contain different numbers of people and advanced 3D human pose estimation techniques. Furthermore, different kinds of algorithms are further subdivided into sub-categories and compared in light of different methodologies. To the best of our knowledge, this is the first such comprehensive survey of the recent progress of 3D human pose estimation and will hopefully facilitate the completion, refinement and applications of 3D human pose estimation.

scholarly journals A Baseline for Cross-Database 3D Human Pose Estimation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3769
Author(s):  
Michał Rapczyński ◽  
Philipp Werner ◽  
Sebastian Handrich ◽  
Ayoub Al-Hamadi
Keyword(s):  
Pose Estimation ◽  
Real Life ◽  
Human Pose Estimation ◽  
3D Pose Estimation ◽  
Multiple Datasets ◽  
In The Wild ◽  
Scale Normalization ◽  
Human Pose ◽  
Future Work ◽  
3D Human Pose Estimation

Vision-based 3D human pose estimation approaches are typically evaluated on datasets that are limited in diversity regarding many factors, e.g., subjects, poses, cameras, and lighting. However, for real-life applications, it would be desirable to create systems that work under arbitrary conditions (“in-the-wild”). To advance towards this goal, we investigated the commonly used datasets HumanEva-I, Human3.6M, and Panoptic Studio, discussed their biases (that is, their limitations in diversity), and illustrated them in cross-database experiments (for which we used a surrogate for roughly estimating in-the-wild performance). For this purpose, we first harmonized the differing skeleton joint definitions of the datasets, reducing the biases and systematic test errors in cross-database experiments. We further proposed a scale normalization method that significantly improved generalization across camera viewpoints, subjects, and datasets. In additional experiments, we investigated the effect of using more or less cameras, training with multiple datasets, applying a proposed anatomy-based pose validation step, and using OpenPose as the basis for the 3D pose estimation. The experimental results showed the usefulness of the joint harmonization, of the scale normalization, and of augmenting virtual cameras to significantly improve cross-database and in-database generalization. At the same time, the experiments showed that there were dataset biases that could not be compensated and call for new datasets covering more diversity. We discussed our results and promising directions for future work.

scholarly journals EfficientPose: Efficient human pose estimation with neural architecture search

2021 ◽  
Author(s):  
Wenqiang Zhang ◽  
Jiemin Fang ◽  
Xinggang Wang ◽  
Wenyu Liu
Keyword(s):  
Pose Estimation ◽  
Spatial Information ◽  
Computational Cost ◽  
Multimedia Applications ◽  
Human Pose Estimation ◽  
Estimation Task ◽  
Neural Architecture ◽  
Great Performance ◽  
Human Pose ◽  
Large Model

AbstractHuman pose estimation from image and video is a key task in many multimedia applications. Previous methods achieve great performance but rarely take efficiency into consideration, which makes it difficult to implement the networks on lightweight devices. Nowadays, real-time multimedia applications call for more efficient models for better interaction. Moreover, most deep neural networks for pose estimation directly reuse networks designed for image classification as the backbone, which are not optimized for the pose estimation task. In this paper, we propose an efficient framework for human pose estimation with two parts, an efficient backbone and an efficient head. By implementing a differentiable neural architecture search method, we customize the backbone network design for pose estimation, and reduce computational cost with negligible accuracy degradation. For the efficient head, we slim the transposed convolutions and propose a spatial information correction module to promote the performance of the final prediction. In experiments, we evaluate our networks on the MPII and COCO datasets. Our smallest model requires only 0.65 GFLOPs with 88.1% [email protected] on MPII and our large model needs only 2 GFLOPs while its accuracy is competitive with the state-of-the-art large model, HRNet, which takes 9.5 GFLOPs.

scholarly journals 3D Single Person Pose Estimation Method Based on Deep Learning

2020 ◽  
Author(s):  
Xinrui Yuan ◽  
Hairong Wang ◽  
Jun Wang
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Estimation Method ◽  
Hybrid Network ◽  
Estimation Methods ◽  
Human Pose Estimation ◽  
Single Person ◽  
Depth Study ◽  
Human Pose ◽  
Set Up

In view of the significant effects of deep learning in graphics and image processing, research on human pose estimation methods using deep learning has attracted much attention, and many method models have been produced one after another. On the basis of tracking and in-depth study of domestic and foreign research results, this paper concentrates on 3D single person pose estimation methods, contrasts and analyzes three methods of end-to-end, staged and hybrid network models, and summarizes the characteristics of the methods. For evaluating method performance, set up an experimental environment, and utilize the Human3.6M data set to test several mainstream methods. The test results indicate that the hybrid network model method has a better performance in the field of human pose estimation.

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