Data Prediction Compensation for Dynamic Target Tracking System Based on BP Neural Network

2018 ◽  
Author(s):  
Shuqing Xu ◽  
Yongrui Qin ◽  
Haiyin Zhou ◽  
Bowen Sun ◽  
Jiongqi Wang
Keyword(s):  
Neural Network ◽  
Target Tracking ◽  
Bp Neural Network ◽  
Tracking System ◽  
Data Prediction ◽  
Dynamic Target

An Industrial Production Line Dynamic Target Tracking System Based on HAAR and CAMSHIFT

2020 ◽  
Vol 34 (11) ◽  
pp. 2059037
Author(s):  
Xuejun Tian ◽  
Haowen Feng ◽  
Jieyan Chen
Keyword(s):  
Target Tracking ◽  
Tracking System ◽  
Raspberry Pi ◽  
Automation System ◽  
Moving Targets ◽  
Embedded Platform ◽  
System A ◽  
Detection And Tracking ◽  
Camshift Algorithm ◽  
Dynamic Target

Aiming at the detection and tracking of moving targets in industrial automation system, a dynamic target tracking algorithm based on HAAR and CAMSHIFT is proposed. A cascade HAAR classifier is designed and trained for tracking targets. CAMSHIFT algorithm is used to track and detect moving targets quickly. The system is tested on Raspberry Pi embedded platform. The results show that the algorithm can detect the target correctly and track the target effectively.

scholarly journals Three-dimensional mandibular motion trajectory-tracking system based on BP neural network

2020 ◽  
Vol 17 (5) ◽  
pp. 5709-5726
Author(s):  
Sukun Tian ◽  
◽  
Ning Dai ◽  
Linlin Li ◽  
Weiwei Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Bp Neural Network ◽  
Tracking System ◽  
Three Dimensional ◽  
Motion Trajectory

scholarly journals Optimization of sand casting performance parameters and missing data prediction

2019 ◽  
Vol 6 (8) ◽  
pp. 181860 ◽  
Author(s):  
Qingwei Xu ◽  
Kaili Xu ◽  
Li Li ◽  
Xiwen Yao
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Bp Neural Network ◽  
Grey Relational Analysis ◽  
Sand Casting ◽  
Performance Parameters ◽  
Relational Analysis ◽  
Data Prediction ◽  
Missing Data Prediction ◽  
Grey Relational

Due to a wide range of applications, sand casting occupies an important position in modern casting practice. The main purpose of this study was to optimize the performance parameters of sand casting based on grey relational analysis and predict the missing data using back propagation (BP) neural network. First, the influence of human factors was eliminated by adopting the objective entropy weight method, which also saved manpower. The larger variation degree in the evaluation indicators, indicating that the evaluated projects had good discrimination in this regard, the larger weight should be given to these evaluation indicators. Second, the performance parameters of sand casting were optimized based on grey relational analysis, providing a reference for sand milling. The larger the grey relational degree, the closer the evaluated project was to the ideal project. Third, this paper provided a new method for determining the number of hidden neurons in a network according to the mean square error of training samples, and venting quality was predicted based on BP neural network. The relevant theory was deduced before predicting missing data, such that there will be a general understanding regarding the prediction principle of BP neural network. Fourth, to demonstrate the validity of BP neural network adopted in the process of missing data prediction, grey system theory was applied to compare the result of missing data prediction.

BP neural Network-Kalman filter fusion method for unmanned aerial vehicle target tracking

2021 ◽  
pp. 095440622098386
Author(s):  
Yongqi Liu ◽  
Liangcheng Nie ◽  
Rui Dong ◽  
Gang Chen
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Target Tracking ◽  
Real Time ◽  
Bp Neural Network ◽  
Target Position ◽  
Difference Matrix ◽  
Onboard Computer ◽  
Center Position ◽  
Kalman Filter Algorithm

The poor real-time performance and target occlusion occurred easily when the UAV was tracking the target. In this paper, a target tracking method based on the Back Propagation neural network fusion Kalman filter algorithm was developed to solve the position prediction problem of the UAV target tracking in real time. Firstly, the target tracking algorithm was used to acquire the center position coordinates of the target on the onboard computer, and then the coordinate difference matrix was constructed to train the BP neural network in real time. Secondly, when the target was occluded by the obstacles judged by the Bhattacharyya coefficient, the BP neural network fusion Kalman filter algorithm was used to accurately predict the center position coordinates of the occluded target. Then the flight speed of UAV was calculated by the deviation between the coordinates of the target and the image center. Finally, the velocity command was sent to the UAV by the onboard computer. The experimental results shown that the target position predicted by BP neural network fusion Kalman filter algorithm was more accurate and robust in predicting the center position coordinates of the target, and the UAV can track the moving target on the ground stably.

The Research of Bionic Robot Dynamic Target Tracking System

2010 ◽  
Vol 33 ◽  
pp. 332-336 ◽  
Author(s):  
P. Wang ◽  
X.F. Ye ◽  
S.C. Kang ◽  
J.L. Xin
Keyword(s):  
Target Tracking ◽  
Tracking System ◽  
Robot Vision ◽  
Wiener Filtering ◽  
Outer Contour ◽  
Automatic Tracking ◽  
Bionic Robot ◽  
Dynamic Target ◽  
Particle Filter Algorithm

In order to improve the quality of the bionic robot vision tracking, the new automatic tracking algorithm system is proposed in this paper. Based on the completed system hardware design and implementing scheme, the scene noise is removed by adaptive wiener filtering. Through the improved sequential particle filter algorithm, the dynamic target tracking is realized. The experiment result shows that the improved algorithm system still can lock the dynamic target accurately under the condition of that the outer contour of target changing and the partial occlusion existing.

Analysis of consumer online resale behavior measurement based on machine learning and BP neural network

2020 ◽  
pp. 1-12
Author(s):  
Xinlu Zou
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Bp Neural Network ◽  
Real Data ◽  
Measurement Model ◽  
Population Diversity ◽  
Training Models ◽  
Network Training ◽  
Data Prediction ◽  
Comparison Results

The reasons for consumers’ resale behavior are complex and sometimes diverse, and the investigation of consumer resale behavior is not a simple matter. Therefore, only through a lot of investigation and inquiry can we reach relevant conclusions. Based on machine learning and BP neural network, this paper constructs a consumer online resale behavior measurement model. The contraction-expansion factor can balance the global search and local search capabilities in different iteration periods, and the differential evolution operator is introduced to solve the problem of lack of population diversity. After building the model, this study collects data through questionnaires, and combines neural network training models to take data training and data prediction. In addition, this study compares and analyzes real data with predicted data, and visually displays the comparison results through statistical graphs. The results show that the method proposed in this paper has certain effects and can provide theoretical references for subsequent related research.

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