scholarly journals Multi-Objective Convolutional Neural Networks for Robot Localisation and 3D Position Estimation in 2D Camera Images

2018 ◽  
Author(s):  
Justinas Miseikis ◽  
Inka Brijacak ◽  
Saeed Yahyanejad ◽  
Kyrre Glette ◽  
Ole Jakob Elle ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Position Estimation ◽  
Multi Objective

A Multi-Objective Evolutionary Approach Based on Graph-in-Graph for Neural Architecture Search of Convolutional Neural Networks

2021 ◽  
pp. 2150035
Author(s):  
Yu Xue ◽  
Pengcheng Jiang ◽  
Ferrante Neri ◽  
Jiayu Liang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Structure ◽  
Ad Hoc ◽  
Network Design Problem ◽  
Multi Objective ◽  
Neural Architecture ◽  
Mutation Operators ◽  
Small Structure ◽  
High Flexibility

With the development of deep learning, the design of an appropriate network structure becomes fundamental. In recent years, the successful practice of Neural Architecture Search (NAS) has indicated that an automated design of the network structure can efficiently replace the design performed by human experts. Most NAS algorithms make the assumption that the overall structure of the network is linear and focus solely on accuracy to assess the performance of candidate networks. This paper introduces a novel NAS algorithm based on a multi-objective modeling of the network design problem to design accurate Convolutional Neural Networks (CNNs) with a small structure. The proposed algorithm makes use of a graph-based representation of the solutions which enables a high flexibility in the automatic design. Furthermore, the proposed algorithm includes novel ad-hoc crossover and mutation operators. We also propose a mechanism to accelerate the evaluation of the candidate solutions. Experimental results demonstrate that the proposed NAS approach can design accurate neural networks with limited size.

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.

Sign in / Sign up

Export Citation Format

Share Document