Dilated Hourglass Networks for Human Pose Estimation

Zhang, Yudong; Liu, Jing; Huang, Kaiyu

doi:10.1109/cac.2018.8623582

Cited by 16 publications

(7 citation statements)

References 12 publications

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“…This modification aims to minimize down-sampling while preserving maximum information. Zhang et al [21] suggest that the design goal of dilated hourglass models (DCM) is to make full use of different feature levels and reduce information loss. Traditional methods tend to use up/down-sampling to expand the perception domain and obtain high-level features.…”

Section: Hourglass Networkmentioning

confidence: 99%

HAR-Net: An Hourglass Attention ResNet Network for Dangerous Driving Behavior Detection

Qu,

Cui,

Yang

2024

Electronics

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Ensuring safety while driving relies heavily on normal driving behavior, making the timely detection of dangerous driving patterns crucial. In this paper, an Hourglass Attention ResNet Network (HAR-Net) is proposed to detect dangerous driving behavior. Uniquely, we separately input optical flow data, RGB data, and RGBD data into the network for spatial–temporal fusion. In the spatial fusion part, we combine ResNet-50 and the hourglass network as the backbone of CenterNet. To improve the accuracy, we add the attention mechanism to the network and integrate center loss into the original Softmax loss. Additionally, a dangerous driving behavior dataset is constructed to evaluate the proposed model. Through ablation and comparative studies, we demonstrate the efficacy of each HAR-Net component. Notably, HAR-Net achieves a mean average precision of 98.84% on our dataset, surpassing other state-of-the-art networks for detecting distracted driving behaviors.

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Section: Hourglass Networkmentioning

confidence: 99%

HAR-Net: An Hourglass Attention ResNet Network for Dangerous Driving Behavior Detection

Qu,

Cui,

Yang

2024

Electronics

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“…Later, many approaches typically generated heatmaps that describe the probability of each key-point at various places. Many researchers experimented deep convolutional neural network-based regression techniques, such as regressing joint coordinates or regressing joint heatmaps [16][17][18][19][20]. In addition, the deep learning algorithms predict the poses from input images, videos, and live events.…”

Section: Related Workmentioning

confidence: 99%

Improvement of Human Pose Estimation and Processing With the Intensive Feature Consistency Network

et al. 2023

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The modeling of human body kye-points is the most significant aspect of pose estimation appropriately. Computer vision algorithm identifies human pose, body-movement, and action in many ways. Most of the previous works taken advantage for finding accuracy or efficiency in terms of speed. However, many techniques suffer for intensive computational demands with low-latency or higher proceeding speed. We have designed a unique approach for single-person pose estimation and action recognition which is well suited for fitness application and mobility activities. The proposed framework has been developed with a base network that provides an initial pose to further refinement through Intensive Feature Consistency (IFC) network. The IFC network enforces high-level constraints on the global body intensity correction and local body part adjustments. The proposed module reduces the impact of body joint movement diversity by interpreting long-term consistent view. We have illustrated the effectiveness of proposed framework through pose estimation accuracy improvement with two benchmark datasets. Which is specified state-of the-art performance of IFC network under the required real-time processing speed on the CPU platform. The IFC network has improved 99.1% of PCK body and 94.7% of PCK torso accuracy under 31 FPS, which is comparatively higher than the existing work.INDEX TERMS Single person pose estimation, intensive feature consistency, global body intensity, local part adjustments, skeleton joint key-points.

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“…However, those methods naturally lack the ability to deal with complex occlusions. Most of the recent works take advantage of deep Convolutional Neural Network (CNN) and follow a regression fashion: regressing joint coordinates [12] or regressing joint heatmaps [13]- [17]. These CNN-based methods either employ multistage architectures [13], [15] to recursively refine estimation results, or build strong backbones [14], [16] to efficiently extract high-level image representations, in order to achieve competitive performance on popular benchmarks [18], [19].…”

Section: Related Work a Human Pose Estimation In Imagesmentioning

confidence: 99%

PosePropagationNet: Towards Accurate and Efficient Pose Estimation in Videos

Liu

Chen

2020

IEEE Access

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We rethink on the contradiction between accuracy and efficiency in the field of video pose estimation. Large networks are typically exploited in previous methods to pursue superior pose estimation results. However, those methods can hardly meet the low-latency requirement for real-time applications because of their computationally expensive nature. We present a novel architecture, PosePropagation-Net (PPN), to generate poses across video frames accurately and efficiently. Instead of extracting temporal cues or knowledge someways to enforce geometric consistency as most of the previous methods do, we explicitly propagate well-estimated pose from the preceding frame to the current frame by leveraging pose propagation mechanism, endowing lightweight networks with the capability of performing accurate pose estimation in videos. The experiments on two large-scale benchmarks for video pose estimation show that our method significantly outperforms previous state-of-the-art methods in both accuracy and efficiency. Compared with the previous best method, our two representative configurations, PPN-Stable and PPN-Swift, achieve 2.5× and 6× FLOPs reduction respectively, as well as significant accuracy improvement. INDEX TERMS Network efficiency, pose propagation mechanism, video pose estimation.

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Dilated Hourglass Networks for Human Pose Estimation

Cited by 16 publications

References 12 publications

HAR-Net: An Hourglass Attention ResNet Network for Dangerous Driving Behavior Detection

HAR-Net: An Hourglass Attention ResNet Network for Dangerous Driving Behavior Detection

Improvement of Human Pose Estimation and Processing With the Intensive Feature Consistency Network

PosePropagationNet: Towards Accurate and Efficient Pose Estimation in Videos

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