scholarly journals Deep Convolutional Neural Networks for Efficient Pose Estimation in Gesture Videos

2015 ◽  
pp. 538-552 ◽  
Author(s):  
Tomas Pfister ◽  
Karen Simonyan ◽  
James Charles ◽  
Andrew Zisserman
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Pose Estimation ◽  
Deep Convolutional Neural Networks

scholarly journals Estimating Player Positions from Padel High-Angle Videos: Accuracy Comparison of Recent Computer Vision Methods

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3368
Author(s):  
Mohammadreza Javadiha ◽  
Carlos Andujar ◽  
Enrique Lacasa ◽  
Angel Ric ◽  
Antonio Susin
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Networks ◽  
Pose Estimation ◽  
Position Estimation ◽  
Estimation Methods ◽  
Large Set ◽  
High Angle ◽  
Deep Convolutional Neural Networks ◽  
Player Positions

The estimation of player positions is key for performance analysis in sport. In this paper, we focus on image-based, single-angle, player position estimation in padel. Unlike tennis, the primary camera view in professional padel videos follows a de facto standard, consisting of a high-angle shot at about 7.6 m above the court floor. This camera angle reduces the occlusion impact of the mesh that stands over the glass walls, and offers a convenient view for judging the depth of the ball and the player positions and poses. We evaluate and compare the accuracy of state-of-the-art computer vision methods on a large set of images from both amateur videos and publicly available videos from the major international padel circuit. The methods we analyze include object detection, image segmentation and pose estimation techniques, all of them based on deep convolutional neural networks. We report accuracy and average precision with respect to manually-annotated video frames. The best results are obtained by top-down pose estimation methods, which offer a detection rate of 99.8% and a RMSE below 5 and 12 cm for horizontal/vertical court-space coordinates (deviations from predicted and ground-truth player positions). These results demonstrate the suitability of pose estimation methods based on deep convolutional neural networks for estimating player positions from single-angle padel videos. Immediate applications of this work include the player and team analysis of the large collection of publicly available videos from international circuits, as well as an inexpensive method to get player positional data in amateur padel clubs.

scholarly journals 3D Head Pose Estimation through Facial Features and Deep Convolutional Neural Networks

2021 ◽  
Vol 66 (2) ◽  
pp. 1757-1770
Author(s):  
Khalil Khan ◽  
Jehad Ali ◽  
Kashif Ahmad ◽  
Asma Gul ◽  
Ghulam Sarwar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Pose Estimation ◽  
Head Pose Estimation ◽  
Facial Features ◽  
Head Pose ◽  
Deep Convolutional Neural Networks ◽  
3D Head Pose Estimation

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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