Human pose estimation method based on single depth image

Smoking detection is an essential part of safety production management. With the wide application of artificial intelligence technology in all kinds of behavior monitoring applications, the technology of real-time monitoring smoking behavior in production areas based on video is essential. In order to carry out smoking detection, it is necessary to analyze the position of key points and posture of the human body in the input image. Due to the diversity of human pose and the complex background in general scene, the accuracy of human pose estimation is not high. To predict accurate human posture information in complex backgrounds, a deep learning network is needed to obtain the feature information of different scales in the input image. The human pose estimation method based on multi-resolution feature parallel network has two parts. The first is to reduce the loss of semantic information by hole convolution and deconvolution in the part of multi-scale feature fusion. The second is to connect different resolution feature maps in the output part to generate the high-quality heat map. To solve the problem of feature loss of previous serial models, more accurate human pose estimation data can be obtained. Experiments show that the accuracy of the proposed method on the coco test set is significantly higher than that of other advanced methods. Accurate human posture estimation results can be better applied to the field of smoking detection, and the smoking behavior can be detected by artificial intelligence, and the alarm will be automatically triggered when the smoking behavior is found.

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Controlled human pose estimation from depth image streams

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops ◽

10.1109/cvprw.2008.4563163 ◽

2008 ◽

Cited By ~ 2

Author(s):

Youding Zhu ◽

Behzad Dariush ◽

Kikuo Fujimura

Keyword(s):

Pose Estimation ◽

Depth Image ◽

Human Pose Estimation ◽

Human Pose ◽

Image Streams

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Human Pose Estimation from Depth Image Using Visibility Estimation and Key Points

Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management. Human Body Modeling and Ergonomics - Lecture Notes in Computer Science ◽

10.1007/978-3-642-39182-8_39 ◽

2013 ◽

pp. 333-342

Author(s):

Sungjin Huh ◽

Gyeonghwan Kim

Keyword(s):

Pose Estimation ◽

Depth Image ◽

Human Pose Estimation ◽

Key Points ◽

Human Pose ◽

Visibility Estimation

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3D Single Person Pose Estimation Method Based on Deep Learning

Fuzzy Systems and Data Mining VI - Frontiers in Artificial Intelligence and Applications ◽

10.3233/faia200726 ◽

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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Human Pose Estimation Method Based on Flexible Model and Deep Learning

Proceedings of the 2nd International Conference on Computer Science and Application Engineering - CSAE '18 ◽

10.1145/3207677.3278035 ◽

2018 ◽

Author(s):

Binghan Liu ◽

Zhenda Li ◽

Xiao Ke

Keyword(s):

Deep Learning ◽

Pose Estimation ◽

Estimation Method ◽

Human Pose Estimation ◽

Human Pose ◽

Flexible Model

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Sequential 3D Human Pose Estimation Using Adaptive Point Cloud Sampling Strategy

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/184 ◽

2021 ◽

Author(s):

Zihao Zhang ◽

Lei Hu ◽

Xiaoming Deng ◽

Shihong Xia

Keyword(s):

Pose Estimation ◽

Point Cloud ◽

Fundamental Problem ◽

Estimation Method ◽

Sampling Strategy ◽

Point Clouds ◽

Human Pose Estimation ◽

Human Pose ◽

New Perspective ◽

3D Human Pose Estimation

3D human pose estimation is a fundamental problem in artificial intelligence, and it has wide applications in AR/VR, HCI and robotics. However, human pose estimation from point clouds still suffers from noisy points and estimated jittery artifacts because of handcrafted-based point cloud sampling and single-frame-based estimation strategies. In this paper, we present a new perspective on the 3D human pose estimation method from point cloud sequences. To sample effective point clouds from input, we design a differentiable point cloud sampling method built on density-guided attention mechanism. To avoid the jitter caused by previous 3D human pose estimation problems, we adopt temporal information to obtain more stable results. Experiments on the ITOP dataset and the NTU-RGBD dataset demonstrate that all of our contributed components are effective, and our method can achieve state-of-the-art performance.

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