scholarly journals DGCN: Dynamic Graph Convolutional Network for Efficient Multi-Person Pose Estimation

2020 ◽  
Vol 34 (07) ◽  
pp. 11924-11931
Author(s):  
Zhongwei Qiu ◽  
Kai Qiu ◽  
Jianlong Fu ◽  
Dongmei Fu
Keyword(s):  
Pose Estimation ◽  
State Of The Art ◽  
Semantic Relations ◽  
Dynamic Graphs ◽  
Dynamic Graph ◽  
Convolutional Network ◽  
Bottom Up ◽  
Multi Level ◽  
Human Pose ◽  
Relative Gains

Multi-person pose estimation aims to detect human keypoints from images with multiple persons. Bottom-up methods for multi-person pose estimation have attracted extensive attention, owing to the good balance between efficiency and accuracy. Recent bottom-up methods usually follow the principle of keypoints localization and grouping, where relations between keypoints are the keys to group keypoints. These relations spontaneously construct a graph of keypoints, where the edges represent the relations between two nodes (i.e., keypoints). Existing bottom-up methods mainly define relations by empirically picking out edges from this graph, while omitting edges that may contain useful semantic relations. In this paper, we propose a novel Dynamic Graph Convolutional Module (DGCM) to model rich relations in the keypoints graph. Specifically, we take into account all relations (all edges of the graph) and construct dynamic graphs to tolerate large variations of human pose. The DGCM is quite lightweight, which allows it to be stacked like a pyramid architecture and learn structural relations from multi-level features. Our network with single DGCM based on ResNet-50 achieves relative gains of 3.2% and 4.8% over state-of-the-art bottom-up methods on COCO keypoints and MPII dataset, respectively.

scholarly journals A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network

2021 ◽  
Vol 11 (9) ◽  
pp. 4241
Author(s):  
Jiahua Wu ◽  
Hyo Jong Lee
Keyword(s):  
Pose Estimation ◽  
Human Body ◽  
State Of The Art ◽  
Estimation Method ◽  
Bottom Up ◽  
Center Point ◽  
Novel Approach ◽  
Body Joints

In bottom-up multi-person pose estimation, grouping joint candidates into the appropriately structured corresponding instance of a person is challenging. In this paper, a new bottom-up method, the Partitioned CenterPose (PCP) Network, is proposed to better cluster the detected joints. To achieve this goal, we propose a novel approach called Partition Pose Representation (PPR) which integrates the instance of a person and its body joints based on joint offset. PPR leverages information about the center of the human body and the offsets between that center point and the positions of the body’s joints to encode human poses accurately. To enhance the relationships between body joints, we divide the human body into five parts, and then, we generate a sub-PPR for each part. Based on this PPR, the PCP Network can detect people and their body joints simultaneously, then group all body joints according to joint offset. Moreover, an improved l1 loss is designed to more accurately measure joint offset. Using the COCO keypoints and CrowdPose datasets for testing, it was found that the performance of the proposed method is on par with that of existing state-of-the-art bottom-up methods in terms of accuracy and speed.

A Multi-Level Network for Human Pose Estimation

2021 ◽  
Author(s):  
Zhanpeng Shao ◽  
Peng Liu ◽  
Youfu Li ◽  
Jianyu Yang ◽  
Xiaolong Zhou
Keyword(s):  
Pose Estimation ◽  
Human Pose Estimation ◽  
Multi Level ◽  
Human Pose

Human pose estimation based on parallel atrous convolution and body structure constraints

2021 ◽  
pp. 1-11
Author(s):  
Min Zhang ◽  
Haijie Yang ◽  
Pengfei Li ◽  
Ming Jiang
Keyword(s):  
Pose Estimation ◽  
The Body ◽  
Human Pose Estimation ◽  
Spatial Relationships ◽  
Body Structure ◽  
Convolutional Network ◽  
In Series ◽  
Human Pose ◽  
Ablation Study ◽  
View Transformation

Human pose estimation is still a challenging task in computer vision, especially in the case of camera view transformation, joints occlusions and overlapping, the task will be of ever-increasing difficulty to achieve success. Most existing methods pass the input through a network, which typically consists of high-to-low resolution sub-networks that are connected in series. Still, during the up-sampling process, the spatial relationships and details might be lost. This paper designs a parallel atrous convolutional network with body structure constraints (PAC-BCNet) to address the problem. Among the mentioned techniques, the parallel atrous convolution (PAC) is constructed to deal with scale changes by connecting multiple different atrous convolution sub-networks in parallel. And it is used to extract features from different scales without reducing the resolution. Besides, the body structure constraints (BC), which enhance the correlation between each keypoint, are constructed to obtain better spatial relationships of the body by designing keypoints constraints sets and improving the loss function. In this work, a comparative experiment of the serial atrous convolution, the parallel atrous convolution, the ablation study with and without body structure constraints are conducted, which reasonably proves the effectiveness of the approach. The model is evaluated on two widely used human pose estimation benchmarks (MPII and LSP). The method achieves better performance on both datasets.

scholarly journals Visual-Semantic Graph Reasoning for Pedestrian Attribute Recognition

2019 ◽  
Vol 33 ◽  
pp. 8634-8641 ◽  
Author(s):  
Qiaozhe Li ◽  
Xin Zhao ◽  
Ran He ◽  
Kaiqi Huang
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Relational Learning ◽  
Spatial Relations ◽  
Semantic Relations ◽  
Prediction Problem ◽  
Convolutional Network ◽  
Semantic Graph ◽  
Spatial Graph ◽  
Attribute Recognition

Pedestrian attribute recognition in surveillance is a challenging task due to poor image quality, significant appearance variations and diverse spatial distribution of different attributes. This paper treats pedestrian attribute recognition as a sequential attribute prediction problem and proposes a novel visual-semantic graph reasoning framework to address this problem. Our framework contains a spatial graph and a directed semantic graph. By performing reasoning using the Graph Convolutional Network (GCN), one graph captures spatial relations between regions and the other learns potential semantic relations between attributes. An end-to-end architecture is presented to perform mutual embedding between these two graphs to guide the relational learning for each other. We verify the proposed framework on three large scale pedestrian attribute datasets including PETA, RAP, and PA100k. Experiments show superiority of the proposed method over state-of-the-art methods and effectiveness of our joint GCN structures for sequential attribute prediction.

scholarly journals Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation

2021 ◽  
Author(s):  
Zhengxiong Luo ◽  
Zhicheng Wang ◽  
Yan Huang ◽  
Liang Wang ◽  
Tieniu Tan ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Human Pose Estimation ◽  
Bottom Up ◽  
Human Pose

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.

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