scholarly journals Improved Convolutional Pose Machines for Human Pose Estimation Using Image Sensor Data

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 718 ◽  
Author(s):  
Baohua Qiang ◽  
Shihao Zhang ◽  
Yongsong Zhan ◽  
Wu Xie ◽  
Tian Zhao
Keyword(s):  
Pose Estimation ◽  
Image Sensor ◽  
Fine Tuning ◽  
Sensor Data ◽  
Convergence Time ◽  
Estimation Accuracy ◽  
Human Pose Estimation ◽  
Training Time ◽  
Human Pose ◽  
Improved Model

In recent years, increasing human data comes from image sensors. In this paper, a novel approach combining convolutional pose machines (CPMs) with GoogLeNet is proposed for human pose estimation using image sensor data. The first stage of the CPMs directly generates a response map of each human skeleton’s key points from images, in which we introduce some layers from the GoogLeNet. On the one hand, the improved model uses deeper network layers and more complex network structures to enhance the ability of low level feature extraction. On the other hand, the improved model applies a fine-tuning strategy, which benefits the estimation accuracy. Moreover, we introduce the inception structure to greatly reduce parameters of the model, which reduces the convergence time significantly. Extensive experiments on several datasets show that the improved model outperforms most mainstream models in accuracy and training time. The prediction efficiency of the improved model is improved by 1.023 times compared with the CPMs. At the same time, the training time of the improved model is reduced 3.414 times. This paper presents a new idea for future research.

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%.

Human Pose Estimation from Pressure Sensor Data

2018 ◽  
pp. 285-290 ◽  
Author(s):  
Leslie Casas ◽  
Chris Mürwald ◽  
Felix Achilles ◽  
Diana Mateus ◽  
Dietrich Huber ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Pressure Sensor ◽  
Sensor Data ◽  
Human Pose Estimation ◽  
Human Pose

scholarly journals Deep Reinforcement Learning for Active Human Pose Estimation

2020 ◽  
Vol 34 (07) ◽  
pp. 10835-10844
Author(s):  
Erik Gärtner ◽  
Aleksis Pirinen ◽  
Cristian Sminchisescu
Keyword(s):  
Reinforcement Learning ◽  
Pose Estimation ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Human Pose Estimation ◽  
Transition Functions ◽  
Processing Step ◽  
Complex Scenes ◽  
Human Pose ◽  
3D Human Pose Estimation

Most 3d human pose estimation methods assume that input – be it images of a scene collected from one or several viewpoints, or from a video – is given. Consequently, they focus on estimates leveraging prior knowledge and measurement by fusing information spatially and/or temporally, whenever available. In this paper we address the problem of an active observer with freedom to move and explore the scene spatially – in ‘time-freeze’ mode – and/or temporally, by selecting informative viewpoints that improve its estimation accuracy. Towards this end, we introduce Pose-DRL, a fully trainable deep reinforcement learning-based active pose estimation architecture which learns to select appropriate views, in space and time, to feed an underlying monocular pose estimator. We evaluate our model using single- and multi-target estimators with strong result in both settings. Our system further learns automatic stopping conditions in time and transition functions to the next temporal processing step in videos. In extensive experiments with the Panoptic multi-view setup, and for complex scenes containing multiple people, we show that our model learns to select viewpoints that yield significantly more accurate pose estimates compared to strong multi-view baselines.

Part Detector Based Human Pose Estimation in Images and Videos

2011 ◽  
Vol 33 (6) ◽  
pp. 1413-1419
Author(s):  
Yan-chao Su ◽  
Hai-zhou Ai ◽  
Shi-hong Lao
Keyword(s):  
Pose Estimation ◽  
Human Pose Estimation ◽  
Human Pose

scholarly journals Deep 3D human pose estimation: A review

2021 ◽  
pp. 103225
Author(s):  
Jinbao Wang ◽  
Shujie Tan ◽  
Xiantong Zhen ◽  
Shuo Xu ◽  
Feng Zheng ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Human Pose Estimation ◽  
Human Pose ◽  
3D Human Pose Estimation
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