scholarly journals Markerless Dog Pose Recognition in the Wild Using ResNet Deep Learning Model

Computers ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 2
Author(s):  
Srinivasan Raman ◽  
Rytis Maskeliūnas ◽  
Robertas Damaševičius
Keyword(s):  
Deep Learning ◽  
Temporal Analysis ◽  
Learning Model ◽  
Mobile App ◽  
Feature Model ◽  
Pose Recognition ◽  
In The Wild ◽  
Spatio Temporal ◽  
And Behavior ◽  
Deep Learning Model

The analysis and perception of behavior has usually been a crucial task for researchers. The goal of this paper is to address the problem of recognition of animal poses, which has numerous applications in zoology, ecology, biology, and entertainment. We propose a methodology to recognize dog poses. The methodology includes the extraction of frames for labeling from videos and deep convolutional neural network (CNN) training for pose recognition. We employ a semi-supervised deep learning model of reinforcement. During training, we used a combination of restricted labeled data and a large amount of unlabeled data. Sequential CNN is also used for feature localization and to find the canine’s motions and posture for spatio-temporal analysis. To detect the canine’s features, we employ image frames to locate the annotations and estimate the dog posture. As a result of this process, we avoid starting from scratch with the feature model and reduce the need for a large dataset. We present the results of experiments on a dataset of more than 5000 images of dogs in different poses. We demonstrated the effectiveness of the proposed methodology for images of canine animals in various poses and behavior. The methodology implemented as a mobile app that can be used for animal tracking.

scholarly journals A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes

2019 ◽  
Vol 34 (4) ◽  
pp. 802-823 ◽  
Author(s):  
Yibin Ren ◽  
Huanfa Chen ◽  
Yong Han ◽  
Tao Cheng ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Spatio Temporal ◽  
Temporal Flow ◽  
Deep Learning Model

scholarly journals Public Opinion Early Warning Agent Model: A Deep Learning Cascade Virality Prediction Model Based on Multi-Feature Fusion

2021 ◽  
Vol 15 ◽  
Author(s):  
Liqun Gao ◽  
Yujia Liu ◽  
Hongwu Zhuang ◽  
Haiyang Wang ◽  
Bin Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Public Opinion ◽  
Prediction Model ◽  
Network Structure ◽  
Early Warning ◽  
Feature Fusion ◽  
Learning Model ◽  
Feature Model ◽  
Underlying Network ◽  
Deep Learning Model

With the rapid popularity of agent technology, a public opinion early warning agent has attracted wide attention. Furthermore, a deep learning model can make the agent more automatic and efficient. Therefore, for the agency of a public opinion early warning task, the deep learning model is very suitable for completing tasks such as popularity prediction or emergency outbreak. In this context, improving the ability to automatically analyze and predict the virality of information cascades is one of the tasks that deep learning model approaches address. However, most of the existing studies sought to address this task by analyzing cascade underlying network structure. Recent studies proposed cascade virality prediction for agnostic-networks (without network structure), but did not consider the fusion of more effective features. In this paper, we propose an innovative cascade virus prediction model named CasWarn. It can be quickly deployed in intelligent agents to effectively predict the virality of public opinion information for different industries. Inspired by the agnostic-network model, this model extracts the key features (independent of the underlying network structure) of an information cascade, including dissemination scale, emotional polarity ratio, and semantic evolution. We use two improved neural network frameworks to embed these features, and then apply the classification task to predict the cascade virality. We conduct comprehensive experiments on two large social network datasets. Furthermore, the experimental results prove that CasWarn can make timely and effective cascade virality predictions and verify that each feature model of CasWarn is beneficial to improve performance.

scholarly journals Gesture Recognition of Somatosensory Interactive Acupoint Massage Based on Image Feature Deep Learning Model

2021 ◽  
Vol 38 (3) ◽  
pp. 565-572
Author(s):  
Yukun Jia ◽  
Rongtao Ding ◽  
Wei Ren ◽  
Jianfeng Shu ◽  
Aixiang Jin
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Gesture Recognition ◽  
Life Quality ◽  
Learning Model ◽  
Training System ◽  
The Body ◽  
Image Feature ◽  
Pose Recognition ◽  
Deep Learning Model

During rehabilitation, many postoperative patients need to perform autonomous massage on time and on demand. Thus, this paper develops an individualized, intelligent, and independent rehabilitation training system for based on image feature deep learning model acupoint massage that excludes human factors. The system, which innovatively integrates massage gesture recognition with human pose recognition. It relies on the binocular depth camera Kinect DK and Google MediaPipe Holistic pipeline to collect the real-time image feature data on joints and gestures of the patient in autonomous massage. Then the system calculates the coordinates of each finger joint, and computes the human poses with VGG-16, a convolutional neural network (CNN); the calculated results are translated, and presented in a virtual reality (VR) model based on Unity 3D, aiming to guide the patient actions in autonomous massage. This is because the image feature of the gesture recognition and pose recognition is hindered, when the hand or the human is occluded by the body or other things, owing to the limited recognition range of the hardware. The experimental results show that, the proposed system could correctly recognize up to 84% of non-occluded gestures, and up to 93% of non-occluded poses; the system also exhibited a good real-time performance, a high operability, and a low cost. Facing the lack of medical staff, our system can effectively improve the life quality of patients.

scholarly journals A novel data-driven model for real-time influenza forecasting

2017 ◽  
Author(s):  
Siva R. Venna ◽  
Amirhossein Tavanaei ◽  
Raju N. Gottumukkala ◽  
Vijay V. Raghavan ◽  
Anthony Maida ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Model ◽  
Data Driven ◽  
Geographical Proximity ◽  
Learning Methods ◽  
Forecasting Methods ◽  
Geographical Regions ◽  
Spatio Temporal ◽  
Deep Learning Model

AbstractWe provide data-driven machine learning methods that are capable of making real-time influenza forecasts that integrate the impacts of climatic factors and geographical proximity to achieve better forecasting performance. The key contributions of our approach are both applying deep learning methods and incorporation of environmental and spatio-temporal factors to improve the performance of the influenza forecasting models. We evaluate the method on Influenza Like Illness (ILI) counts and climatic data, both publicly available data sets. Our proposed method outperforms existing known influenza forecasting methods in terms of their Mean Absolute Percentage Error and Root Mean Square Error. The key advantages of the proposed data-driven methods are as following: (1) The deep-learning model was able to effectively capture the temporal dynamics of flu spread in different geographical regions, (2) The extensions to the deep-learning model capture the influence of external variables that include the geographical proximity and climatic variables such as humidity, temperature, precipitation and sun exposure in future stages, (3) The model consistently performs well for both the city scale and the regional scale on the Google Flu Trends (GFT) and Center for Disease Control (CDC) flu counts. The results offer a promising direction in terms of both data-driven forecasting methods and capturing the influence of spatio-temporal and environmental factors for influenza forecasting methods.

scholarly journals Multi-Task Fusion Deep Learning Model for Short-Term Intersection Operation Performance Forecasting

2021 ◽  
Vol 13 (10) ◽  
pp. 1919
Author(s):  
Deqi Chen ◽  
Xuedong Yan ◽  
Xiaobing Liu ◽  
Liwei Wang ◽  
Fengxiao Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Sampling Rate ◽  
Learning Model ◽  
Operation Performance ◽  
Floating Car Data ◽  
Proposed Model ◽  
Intersection Operation ◽  
Spatio Temporal ◽  
Deep Learning Model

Urban road intersection bottleneck has become an important factor in causing traffic delay and restricting traffic efficiency. It is essential to explore the prediction of the operating performance at intersections in real-time and formulate corresponding strategies to alleviate intersection delay. However, because of the sophisticated intersection traffic condition, it is difficult to capture the intersection traffic Spatio-temporal features by the traditional data and prediction methods. The development of big data technology and the deep learning model provides us a good chance to address this challenge. Therefore, this paper proposes a multi-task fusion deep learning (MFDL) model based on massive floating car data to effectively predict the passing time and speed at intersections over different estimation time granularity. Moreover, the grid model and the fuzzy C-means (FCM) clustering method are developed to identify the intersection area and derive a set of key Spatio-temporal traffic parameters from floating car data. In order to validate the effectiveness of the proposed model, the floating car data from ten intersections of Beijing with a sampling rate of 3s are adopted for the training and test process. The experiment result shows that the MFDL model enables us to capture the Spatio-temporal and topology feature of the traffic state efficiently. Compared with the traditional prediction method, the proposed model has the best prediction performance. The interplay between these two targeted prediction variables can significantly improve prediction accuracy and efficiency. Thereby, this method predicts the intersection operation performance in real-time and can provide valuable insights for traffic managers to improve the intersection’s operation efficiency.

Sign in / Sign up

Export Citation Format

Share Document