scholarly journals Olympic Games Event Recognition via Transfer Learning with Photobombing Guided Data Augmentation

2021 ◽  
Vol 7 (2) ◽  
pp. 12
Author(s):  
Yousef I. Mohamad ◽  
Samah S. Baraheem ◽  
Tam V. Nguyen
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Olympic Games ◽  
Event Recognition ◽  
Surveillance Systems ◽  
Video Captioning ◽  
Practical Applications ◽  
Sport Events ◽  
The Olympic Games

Automatic event recognition in sports photos is both an interesting and valuable research topic in the field of computer vision and deep learning. With the rapid increase and the explosive spread of data, which is being captured momentarily, the need for fast and precise access to the right information has become a challenging task with considerable importance for multiple practical applications, i.e., sports image and video search, sport data analysis, healthcare monitoring applications, monitoring and surveillance systems for indoor and outdoor activities, and video captioning. In this paper, we evaluate different deep learning models in recognizing and interpreting the sport events in the Olympic Games. To this end, we collect a dataset dubbed Olympic Games Event Image Dataset (OGED) including 10 different sport events scheduled for the Olympic Games Tokyo 2020. Then, the transfer learning is applied on three popular deep convolutional neural network architectures, namely, AlexNet, VGG-16 and ResNet-50 along with various data augmentation methods. Extensive experiments show that ResNet-50 with the proposed photobombing guided data augmentation achieves 90% in terms of accuracy.

The Effects of Significant International Sports Events on Hungarian Suicide Rates

Crisis ◽  
2016 ◽  
Vol 37 (2) ◽  
pp. 148-154 ◽  
Author(s):  
Karoly Bozsonyi ◽  
Peter Osvath ◽  
Sandor Fekete ◽  
Lajos Bálint
Keyword(s):  
Significant Relationship ◽  
Olympic Games ◽  
Arima Model ◽  
Age Groups ◽  
The Elderly ◽  
Intervention Analysis ◽  
Middle Aged ◽  
Sports Events ◽  
Sport Events ◽  
The Olympic Games

Abstract. Background: Several studies found a significant relationship between important sport events and suicidal behavior. Aims: We set out to investigate whether there is a significant relationship between the raw suicide rate and the most important international sports events (Olympic Games, FIFA World Cup, UEFA European Championship) in such an achievement-oriented society as the Hungarian one, where these sport events receive great attention. Method: We examined suicide cases occurring over 15,706 days between January 1, 1970, and December 31, 2012 (43 years), separately for each gender. Because of the age-specific characteristics of suicide, the effects of these sport events were analyzed for the middle-aged (30–59 years old) and the elderly (over 60 years old) generations as well as for gender-specific population groups. The role of international sport events was examined with the help of time-series intervention analysis after cyclical and seasonal components were removed. Intervention analysis was based on the ARIMA model. Results: Our results showed that only the Olympic Games had a significant effect in the middle-aged population. Neither in the older male nor in any of the female age groups was a relationship between suicide and Olympic Games detected. Conclusion: The Olympic Games seem to decrease the rate of suicide among middle-aged men, slightly but significantly.

Metaphorik im Kommunikationsbereich der Sportfachsprachlichkeit

Fachsprache ◽  
2019 ◽  
Vol 41 (S1) ◽  
pp. 87-100
Author(s):  
Wenke Mückel
Keyword(s):  
Olympic Games ◽  
Special Kind ◽  
European Football ◽  
Sport Events ◽  
The Olympic Games

Metaphorical elements are a highly productive language means in live reports about sport events on TV. They occur in different relations to what is simultaneously seen on screen and depend on the reporter as well as on the special kind of sport. But nevertheless, general structures and functions of metaphors in those medium-bound oral texts can be indicated; as one of the markers they contribute to what is often called language of sport or maybe rather communicative template of sport. Examples taken from TV reports of the European Football Championship and the Olympic Games (both took place in 2016) are used to illustrate this character of metaphorical expressions in sport reports on TV.

Data augmentation and transfer learning strategies for reaction prediction in low chemical data regimes

2021 ◽  
Author(s):  
Yun Zhang ◽  
Ling Wang ◽  
Xinqiao Wang ◽  
Chengyun Zhang ◽  
Jiamin Ge ◽  
...  
Keyword(s):  
Organic Chemistry ◽  
Deep Learning ◽  
Drug Discovery ◽  
Research And Development ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Chemical Reactions ◽  
Data Augmentation ◽  
Learning Method ◽  
Reaction Prediction

An effective and rapid deep learning method to predict chemical reactions contributes to the research and development of organic chemistry and drug discovery.

scholarly journals Comparative Analysis on Deep Learning Approaches for Heavy-Vehicle Detection based on Data Augmentation and Transfer-Learning techniques

2021 ◽  
Vol 13 (3) ◽  
pp. 809-820
Author(s):  
V. Sowmya ◽  
R. Radha
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Real Time ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Traffic Management ◽  
Data Augmentation ◽  
Vehicle Detection ◽  
Heavy Vehicles ◽  
Detection And Recognition

Vehicle detection and recognition require demanding advanced computational intelligence and resources in a real-time traffic surveillance system for effective traffic management of all possible contingencies. One of the focus areas of deep intelligent systems is to facilitate vehicle detection and recognition techniques for robust traffic management of heavy vehicles. The following are such sophisticated mechanisms: Support Vector Machine (SVM), Convolutional Neural Networks (CNN), Regional Convolutional Neural Networks (R-CNN), You Only Look Once (YOLO) model, etcetera. Accordingly, it is pivotal to choose the precise algorithm for vehicle detection and recognition, which also addresses the real-time environment. In this study, a comparison of deep learning algorithms, such as the Faster R-CNN, YOLOv2, YOLOv3, and YOLOv4, are focused on diverse aspects of the features. Two entities for transport heavy vehicles, the buses and trucks, constitute detection and recognition elements in this proposed work. The mechanics of data augmentation and transfer-learning is implemented in the model; to build, execute, train, and test for detection and recognition to avoid over-fitting and improve speed and accuracy. Extensive empirical evaluation is conducted on two standard datasets such as COCO and PASCAL VOC 2007. Finally, comparative results and analyses are presented based on real-time.

scholarly journals A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7543
Author(s):  
Bogdan Ilie Sighencea ◽  
Rareș Ion Stanciu ◽  
Cătălin Daniel Căleanu
Keyword(s):  
Deep Learning ◽  
Performance Metrics ◽  
Evaluation Process ◽  
Surveillance Systems ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Trajectory Prediction ◽  
Processing Technologies ◽  
Practical Applications ◽  
New Research

Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the street is a key task in many areas, e.g., self-driving auto vehicles, mobile robots or advanced surveillance systems, and they still represent a technological challenge. The performance of state-of-the-art pedestrian trajectory prediction methods currently benefits from the advancements in sensors and associated signal processing technologies. The current paper reviews the most recent deep learning-based solutions for the problem of pedestrian trajectory prediction along with employed sensors and afferent processing methodologies, and it performs an overview of the available datasets, performance metrics used in the evaluation process, and practical applications. Finally, the current work exposes the research gaps from the literature and outlines potential new research directions.

scholarly journals Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

Diagnostics ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 417 ◽  
Author(s):  
Mohammad Farukh Hashmi ◽  
Satyarth Katiyar ◽  
Avinash G Keskar ◽  
Neeraj Dhanraj Bokde ◽  
Zong Woo Geem
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Medical Center ◽  
Training Dataset ◽  
Test Accuracy ◽  
X Rays ◽  
Learning Models ◽  
Novel Approach ◽  
Unseen Data

Pneumonia causes the death of around 700,000 children every year and affects 7% of the global population. Chest X-rays are primarily used for the diagnosis of this disease. However, even for a trained radiologist, it is a challenging task to examine chest X-rays. There is a need to improve the diagnosis accuracy. In this work, an efficient model for the detection of pneumonia trained on digital chest X-ray images is proposed, which could aid the radiologists in their decision making process. A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way. This approach is a supervised learning approach in which the network predicts the result based on the quality of the dataset used. Transfer learning is used to fine-tune the deep learning models to obtain higher training and validation accuracy. Partial data augmentation techniques are employed to increase the training dataset in a balanced way. The proposed weighted classifier is able to outperform all the individual models. Finally, the model is evaluated, not only in terms of test accuracy, but also in the AUC score. The final proposed weighted classifier model is able to achieve a test accuracy of 98.43% and an AUC score of 99.76 on the unseen data from the Guangzhou Women and Children’s Medical Center pneumonia dataset. Hence, the proposed model can be used for a quick diagnosis of pneumonia and can aid the radiologists in the diagnosis process.

scholarly journals Deep person re-identification in UAV images

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Aleksei Grigorev ◽  
Zhihong Tian ◽  
Seungmin Rho ◽  
Jianxin Xiong ◽  
Shaohui Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Group Learning ◽  
Identification Problem ◽  
Gaussian Mixture ◽  
Surveillance Systems ◽  
Deep Convolutional Neural Networks ◽  
Recent Success ◽  
Proposed Model ◽  
Uav Images

AbstractThe person re-identification is one of the most significant problems in computer vision and surveillance systems. The recent success of deep convolutional neural networks in image classification has inspired researchers to investigate the application of deep learning to the person re-identification. However, the huge amount of research on this problem considers classical settings, where pedestrians are captured by static surveillance cameras, although there is a growing demand for analyzing images and videos taken by drones. In this paper, we aim at filling this gap and provide insights on the person re-identification from drones. To our knowledge, it is the first attempt to tackle this problem under such constraints. We present the person re-identification dataset, named DRone HIT (DRHIT01), which is collected by using a drone. It contains 101 unique pedestrians, which are annotated with their identities. Each pedestrian has about 500 images. We propose to use a combination of triplet and large-margin Gaussian mixture (L-GM) loss to tackle the drone-based person re-identification problem. The proposed network equipped with multi-branch design, channel group learning, and combination of loss functions is evaluated on the DRHIT01 dataset. Besides, transfer learning from the most popular person re-identification datasets is evaluated. Experiment results demonstrate the importance of transfer learning and show that the proposed model outperforms the classic deep learning approach.

scholarly journals Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yixiang Deng ◽  
Lu Lu ◽  
Laura Aponte ◽  
Angeliki M. Angelidi ◽  
Vera Novak ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Deep Learning ◽  
Blood Glucose ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Generative Models ◽  
Patient Specific ◽  
Learning Methods ◽  
Glucose Levels

AbstractAccurate prediction of blood glucose variations in type 2 diabetes (T2D) will facilitate better glycemic control and decrease the occurrence of hypoglycemic episodes as well as the morbidity and mortality associated with T2D, hence increasing the quality of life of patients. Owing to the complexity of the blood glucose dynamics, it is difficult to design accurate predictive models in every circumstance, i.e., hypo/normo/hyperglycemic events. We developed deep-learning methods to predict patient-specific blood glucose during various time horizons in the immediate future using patient-specific every 30-min long glucose measurements by the continuous glucose monitoring (CGM) to predict future glucose levels in 5 min to 1 h. In general, the major challenges to address are (1) the dataset of each patient is often too small to train a patient-specific deep-learning model, and (2) the dataset is usually highly imbalanced given that hypo- and hyperglycemic episodes are usually much less common than normoglycemia. We tackle these two challenges using transfer learning and data augmentation, respectively. We systematically examined three neural network architectures, different loss functions, four transfer-learning strategies, and four data augmentation techniques, including mixup and generative models. Taken together, utilizing these methodologies we achieved over 95% prediction accuracy and 90% sensitivity for a time period within the clinically useful 1 h prediction horizon that would allow a patient to react and correct either hypoglycemia and/or hyperglycemia. We have also demonstrated that the same network architecture and transfer-learning methods perform well for the type 1 diabetes OhioT1DM public dataset.

scholarly journals StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial Applications

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1497
Author(s):  
Harold Achicanoy ◽  
Deisy Chaves ◽  
Maria Trujillo
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Industrial Applications ◽  
Generative Models ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Augmentation Strategy ◽  
Synthetic Images ◽  
The Impact

Deep learning applications on computer vision involve the use of large-volume and representative data to obtain state-of-the-art results due to the massive number of parameters to optimise in deep models. However, data are limited with asymmetric distributions in industrial applications due to rare cases, legal restrictions, and high image-acquisition costs. Data augmentation based on deep learning generative adversarial networks, such as StyleGAN, has arisen as a way to create training data with symmetric distributions that may improve the generalisation capability of built models. StyleGAN generates highly realistic images in a variety of domains as a data augmentation strategy but requires a large amount of data to build image generators. Thus, transfer learning in conjunction with generative models are used to build models with small datasets. However, there are no reports on the impact of pre-trained generative models, using transfer learning. In this paper, we evaluate a StyleGAN generative model with transfer learning on different application domains—training with paintings, portraits, Pokémon, bedrooms, and cats—to generate target images with different levels of content variability: bean seeds (low variability), faces of subjects between 5 and 19 years old (medium variability), and charcoal (high variability). We used the first version of StyleGAN due to the large number of publicly available pre-trained models. The Fréchet Inception Distance was used for evaluating the quality of synthetic images. We found that StyleGAN with transfer learning produced good quality images, being an alternative for generating realistic synthetic images in the evaluated domains.

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