A Novel Camera Calibration Method for Binocular Vision Based on Improved RBF Neural Network

2017 ◽  
pp. 439-448
Author(s):  
Weike Liu ◽  
Ju Huo ◽  
Xing Zhou ◽  
Ming Yang
Keyword(s):  
Neural Network ◽  
Camera Calibration ◽  
Binocular Vision ◽  
Rbf Neural Network ◽  
Calibration Method

A Calibration Method for Binocular Vision System Based on CPSO-BP Netural Network

2014 ◽  
Vol 490-491 ◽  
pp. 1686-1691
Author(s):  
Jun Zhou ◽  
Tao Xia ◽  
Ting Ting Wang ◽  
Hua Li Li ◽  
Yu Ping Fu
Keyword(s):  
Neural Network ◽  
Binocular Vision ◽  
Bp Neural Network ◽  
Vision System ◽  
Back Propagation ◽  
Calibration Method ◽  
Image Feature ◽  
Training Set ◽  
Swarm Optimization ◽  
Binocular Vision System

This paper presents a new calibration method for binocular vision system, based on CPSO-BP neural network. Firstly, the training set of the back propagation (BP) neural network is formed by the image feature point extracted from the binocular vision system. Then the cooperate particle swarm optimization (CPSO) algorithm is introduced to optimize the weights of the BP neural network, making the network with a stronger ability of the global optimization. Experimental results demonstrate that the proposed CPSO-BP-based algorithm has a higher calibration precision than the traditional BP-based calibration method.

scholarly journals Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Li Wang ◽  
Shimin Lin ◽  
Jingfeng Yang ◽  
Nanfeng Zhang ◽  
Ji Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Traffic Congestion ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Rbf Neural Network ◽  
Calibration Method ◽  
Theory Model ◽  
Point Of View ◽  
Urban Traffic

Traffic congestion is a common problem in many countries, especially in big cities. At present, China’s urban road traffic accidents occur frequently, the occurrence frequency is high, the accident causes traffic congestion, and accidents cause traffic congestion and vice versa. The occurrence of traffic accidents usually leads to the reduction of road traffic capacity and the formation of traffic bottlenecks, causing the traffic congestion. In this paper, the formation and propagation of traffic congestion are simulated by using the improved medium traffic model, and the control strategy of congestion dissipation is studied. From the point of view of quantitative traffic congestion, the paper provides the fact that the simulation platform of urban traffic integration is constructed, and a feasible data analysis, learning, and parameter calibration method based on RBF neural network is proposed, which is used to determine the corresponding decision support system. The simulation results prove that the control strategy proposed in this paper is effective and feasible. According to the temporal and spatial evolution of the paper, we can see that the network has been improved on the whole.

Agricultural Machinery Operation Posture Rapid Detection Intelligent Sensor Calibration Method Based on RBF Neural Network

2013 ◽  
Vol 373-375 ◽  
pp. 932-935 ◽  
Author(s):  
Nan Feng Zhang ◽  
Jing Feng Yang ◽  
Yue Ju Xue ◽  
Zhong Li ◽  
Xiao Lin Huang
Keyword(s):  
Neural Network ◽  
Rapid Detection ◽  
Rbf Neural Network ◽  
Calibration Method ◽  
Measurement Unit ◽  
Sensor Calibration ◽  
Test Results ◽  
Agricultural Machinery ◽  
Intelligent Sensor ◽  
Status Data

Based on agricultural machinery body posture detection parameters and wheels gesture detection parameters collected by gyro inertial measurement unit, an agricultural machinery operation posture rapid detection method is proposed in this paper. The test results calibrated by RBF neural network show that, the test results of the method are accurate and available, and the method is effective and available for real-time body and wheel status data to further understand the agricultural machinery.

Genetic Optimized BP Network Method for Camera Calibration in Binocular Vision

2013 ◽  
Vol 756-759 ◽  
pp. 3404-3409 ◽  
Author(s):  
Xing Chen Hu ◽  
Hong Lei An ◽  
Hong Xu Ma ◽  
Hai Bin Xie ◽  
Hong Tao Xue
Keyword(s):  
Neural Network ◽  
Camera Calibration ◽  
Binocular Vision ◽  
Improved Genetic Algorithm ◽  
Bp Network ◽  
The Neural Network ◽  
Calibration Methods ◽  
Network Methods ◽  
Complex Mathematical Model ◽  
Approximation Capability

Camera calibration is the first step of positioning using binocular vision. Owning to the approximation capability of the neural network, a complex mathematical model needed by traditional calibration methods can be avoided. However the general neural network methods have their drawbacks to reduce its accuracy. This paper presents searching algorithm for the best structure and parameters of a neural network using an improved genetic algorithm (GA). The experiments show that this method can be used to establish a mapping between 2D coordinates and 3D coordinates directly and accurately, which is better than traditional calibration and general BP network methods.

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