scholarly journals Automation of Cricket Scoreboard by Recognizing Umpire Gestures

2020 ◽  
Vol 6 (7) ◽  
pp. 1-7
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Training Time ◽  
System A ◽  
Pose Detection ◽  
Random Weights ◽  
Majority Voter ◽  
Visual Database ◽  
Novel Method ◽  
Combined Classifier

Imagine how tiresome it is for the scorers to update the scoreboard after each ball delivery during a cricket match. They need to be alert during any point in the match, watch every single ball, record ball by ball events, modify the score and coordinate with the umpire the entire time. A system that can update the scoreboard automatically after every ball will lessen their effort by half; the time taken for the updation and the chances of errors will also be reduced. A novel method for umpire pose detection for updating the cricket scoreboard during real-time cricket matches is suggested in this work. The proposed system identifies the events happening in the pitch by recognizing the gestures of the umpire and then updates the scoreboard accordingly. The concept of transfer learning is used to accelerate the training of neural network for feature extraction. The Inception V3 network pretrained on the visual database ImageNet is culled as the primary prospect for feature extraction. Instead of initializing the model with random weights, initializing it with the pretrained weights reduces the training time and hence is more efficient. The proposed system is a combination of two SVM classifiers. The leadoff classifier tells apart the images that contain an umpire from the non-umpire images. These ‘umpire’ images are then carried forward to the event detection classifier while the ‘non-umpire’ images are repudiated. The second classifier is able to identify four gestures – ‘Six’, ‘Wide’, ‘No ball’ and ‘Out’ from the images, following which the scoreboard is updated. In addition to these four classes, one more label is defined to group those umpire frames within which the umpire does not show any signal, namely the ‘No Action’ class. The cricket video given as input is first split into number of shots and each frame is considered as a test image for the combined classifier system. A majority voter is used to confirm the final classification result which decreases the chances of misclassifications. The preliminary results suggest that the intended system is efficacious for the purpose of automating the updation of scoreboard during real time cricket matches.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Multi-step real-time wind speed prediction based on convolution memory network

2021 ◽  
pp. 0309524X2199826
Author(s):  
Guowei Cai ◽  
Yuqing Yang ◽  
Chao Pan ◽  
Dian Wang ◽  
Fengjiao Yu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Wind Speed ◽  
Real Time ◽  
Wind Power ◽  
Spatial Correlation ◽  
Prediction Accuracy ◽  
Spatial Information ◽  
Spatial Association ◽  
Spatial Feature ◽  
Time Prediction

Multi-step real-time prediction based on the spatial correlation of wind speed is a research hotspot for large-scale wind power grid integration, and this paper proposes a multi-location multi-step wind speed combination prediction method based on the spatial correlation of wind speed. The correlation coefficients were determined by gray relational analysis for each turbine in the wind farm. Based on this, timing-control spatial association optimization is used for optimization and scheduling, obtaining spatial information on the typical turbine and its neighborhood information. This spatial information is reconstructed to improve the efficiency of spatial feature extraction. The reconstructed spatio-temporal information is input into a convolutional neural network with memory cells. Spatial feature extraction and multi-step real-time prediction are carried out, avoiding the problem of missing information affecting prediction accuracy. The method is innovative in terms of both efficiency and accuracy, and the prediction accuracy and generalization ability of the proposed method is verified by predicting wind speed and wind power for different wind farms.

scholarly journals An LSTM-Based Method Considering History and Real-Time Data for Passenger Flow Prediction

2020 ◽  
Vol 10 (11) ◽  
pp. 3788 ◽  
Author(s):  
Qi Ouyang ◽  
Yongbo Lv ◽  
Jihui Ma ◽  
Jing Li
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Short Term Memory ◽  
Historical Data ◽  
Time Interval ◽  
Information Coding ◽  
Time Data ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Real Time Data

With the development of big data and deep learning, bus passenger flow prediction considering real-time data becomes possible. Real-time traffic flow prediction helps to grasp real-time passenger flow dynamics, provide early warning for a sudden passenger flow and data support for real-time bus plan changes, and improve the stability of urban transportation systems. To solve the problem of passenger flow prediction considering real-time data, this paper proposes a novel passenger flow prediction network model based on long short-term memory (LSTM) networks. The model includes four parts: feature extraction based on Xgboost model, information coding based on historical data, information coding based on real-time data, and decoding based on a multi-layer neural network. In the feature extraction part, the data dimension is increased by fusing bus data and points of interest to improve the number of parameters and model accuracy. In the historical information coding part, we use the date as the index in the LSTM structure to encode historical data and provide relevant information for prediction; in the real-time data coding part, the daily half-hour time interval is used as the index to encode real-time data and provide real-time prediction information; in the decoding part, the passenger flow data for the next two 30 min interval outputs by decoding all the information. To our best knowledge, it is the first time to real-time information has been taken into consideration in passenger flow prediction based on LSTM. The proposed model can achieve better accuracy compared to the LSTM and other baseline methods.

scholarly journals A Novel Method for Detecting Abnormal Energy Data in Building Energy Monitoring System

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Liang Zhao
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Real Time ◽  
Difference Method ◽  
Building Energy ◽  
Energy Monitoring ◽  
Order Difference ◽  
Energy Data ◽  
First Order ◽  
Novel Method

This paper presents a novel abnormal data detecting algorithm based on the first order difference method, which could be used to find out outlier in building energy consumption platform real time. The principle and criterion of methodology are discussed in detail. The results show that outlier in cumulative power consumption could be detected by our method.

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