scholarly journals Parking Space Recognition Method Based on Parking Space Feature Construction in the Scene of Autonomous Valet Parking

2021 ◽  
Vol 11 (6) ◽  
pp. 2759
Author(s):  
Shidian Ma ◽  
Weifeng Fang ◽  
Haobin Jiang ◽  
Mu Han ◽  
Chenxu Li
Keyword(s):  
Template Matching ◽  
Recognition Algorithm ◽  
Feature Construction ◽  
Parking Space ◽  
Light Conditions ◽  
Recognition Method ◽  
Matching Algorithm ◽  
Information Interaction ◽  
Similarity Calculation ◽  
Space Recognition

At present, the realization of autonomous valet parking (AVP) technology does not achieve information interaction between the parking spaces and vehicles, and accurate parking spaces information perception cannot be obtained when the accuracy of the search is not precise. In addition, when using the camera vision to identify the parking spaces, traditional parking space features such as parking lines and parking angles recognition are susceptible to light and environment. Especially when the vehicle nearby partially occupies the parking space to be parked, it is not easy to determine whether it is a valid empty parking space. This paper proposes a parking space recognition method based on parking space features in the scene of AVP. By constructing the multi-dimensional features containing the parking space information, the cameras are used to extract features’ contour, locate features’ position and recognize features. In this paper, a new similarity calculation formula is proposed to recognize the stained features through template matching algorithm. According to the relative position relationship between the feature and parking space, the identification of effective empty parking spaces and their boundaries is realized. The experimental results show that compared with the recognition of traditional parking lines and parking angles, this method can identify effective empty parking spaces even when the light conditions are complex and the parking spaces are partially occupied by adjacent vehicles, which simplifies the recognition algorithm and improves the reliability of the parking spaces identification.

Synergetic Stereo Matching Algorithm for Occlusion and Reversal Position

2003 ◽  
Vol 7 (2) ◽  
pp. 178-188 ◽  
Author(s):  
Tohru Irie ◽  
◽  
Hiroshi Maeda ◽  
Norikazu Ikoma
Keyword(s):  
Template Matching ◽  
Order Parameter ◽  
Stereo Matching ◽  
Recognition Algorithm ◽  
Parameter Design ◽  
Pattern Recognition Algorithm ◽  
Matching Algorithm ◽  
Left And Right ◽  
Attention Parameter ◽  
Two Parameters

We propose a stereo matching algorithm based on a pattern recognition algorithm using synergetics. Matching between left and right images is represented by the differential equations with order parameter and attention parameter. This algorithm gives us a free hand designing the two parameters. This paper presents parameter design and experiment results confirming that the proposed algorithm has high matching precision compared to the conventional template matching and DP matching.

Research of an Improved Snail Image Recognition Method Based on Grayscale Template Matching

2013 ◽  
Vol 433-435 ◽  
pp. 700-704
Author(s):  
Yin E Zhang
Keyword(s):  
Template Matching ◽  
Source Image ◽  
Complex Environment ◽  
Recognition Method ◽  
Matching Method ◽  
Matching Accuracy ◽  
Matching Algorithm ◽  
Template Image ◽  
Traditional Algorithm ◽  
Template Selection

As the lack in the accuracy and speed of the template matching algorithm for the snail image in the complex environment, the snail source image and the template image have the appropriate scaling in order to improve their sizes in the traditional algorithm. The new algorithm avoids the very big and accurate characteristics about the snail images through shrinking the source images down. The grayscale template matching method is put forward based on the traditional template selection set to prevent that the error caused by human factors on the selected template, the redundancy between the templates is removed in a large extent, further the accuracy of the matching is improved, and the matching time is reduced greatly in the case of matching accuracy guarantee.

scholarly journals Automatic Vehicle Tracking System Based on Fixed Thresholding and Histogram Based Edge Processing

2015 ◽  
Vol 5 (4) ◽  
pp. 869
Author(s):  
N. Shobha Rani ◽  
Neethu O. P. ◽  
Nila Ponnath
Keyword(s):  
Control Systems ◽  
Traffic Control ◽  
Template Matching ◽  
Tracking System ◽  
Recognition Algorithm ◽  
Vehicle Tracking ◽  
Matching Algorithm ◽  
Drastic Increase ◽  
Threshold Technique ◽  
Vehicle Tracking System

Automatic detection, extraction and recognition of vehicle number plate region in traffic control systems is one of the prominent application in Computer vision. The drastic increase in number of vehicles in the current generation greatly increases the complexity in tracking the vehicles through the human visual system, manual procedure of controlling traffic and enforcement of various laws and rules is not sufficient for smooth control of traffic. This urges the need for development of technology that can automate this process. This paper mainly focuses on the development of an automatic number plate extraction and recognition algorithm by incorporating constructs like edge detection, horizontal and vertical edge processing using fixed threshold technique. The extracted number plate region is again processed using template matching algorithm for the recognition of the characters embossed on the number plate with respect to every individual piece of number plate. The algorithm developed has achieved an accuracy of around 100% and works for both front and rear images of the car.

Objective Recognition Algorithm of Remote Sensing Image Based on Wavelet Transform

2013 ◽  
Vol 385-386 ◽  
pp. 1402-1406
Author(s):  
Ben Tu Li ◽  
Zhi Chao Yuan
Keyword(s):  
Template Matching ◽  
Low Frequency ◽  
Remote Sensing Image ◽  
Recognition Algorithm ◽  
Inverse Transformation ◽  
Orthogonal Wavelet ◽  
A Algorithm ◽  
Matching Algorithm ◽  
Noise Interference ◽  
Accurate Position

The traditional template matching algorithm which has a high time complexity, is susceptible to noise. This paper proposed a algorithm which based on the features of continuous orthogonal wavelet Daubechies, decomposed images to be identified to multiple layers using wavelet tranformation. In order to get matching position, we select matching template in low-frequency images. Then get matched position in higher layers after doing inverse transformation to low-frequency image. Finally, accurate position of matching template will get in original images. The algorithm not only can reduce the searching time when images are matched, but also can filter out a certain amount of noise, and so reduce the noise interference.

An Improved Character Recognition Algorithm for License Plate Based on BP Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 202-207 ◽  
Author(s):  
Zhong Qu ◽  
Qing-li Chang ◽  
Chang-zhi Chen ◽  
Li-dan Lin
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Template Matching ◽  
Recognition Algorithm ◽  
License Plate ◽  
Recognition Method ◽  
Neural Network Method ◽  
Identification Rate ◽  
Advantages And Disadvantages ◽  
Network Method

License plate character recognition is the basis of automatic license plate recognition (LPR) and it plays an important role in LPR. In this paper, we considered the advantages and disadvantages of the neural network method and proposed an improved approach of character recognition for license plates. In our approach, firstly, license plates were segmented into character pictures by using the algorithm which combines the projection and morphology. Secondly, with a focus on each character picture, recognition results determined by the calculation of the new recognition algorithm were as a reflection of the different features of every kind of character image. Then, character image samples were classified according to different light environment and character type itself. Finally, we used extracted features vectors to train the BP (error back propagation) neural network with adding noise relatively. Due to the influence of environmental factors or character images themselves will bring font discrepancy, font slant, stroke connection and so on, compared with template matching recognition method, neural network method has relatively great space to enhance the recognition effect. In the experiment, we used 1000 license plates images that had been successfully located. Of which, 11800 character images have been successfully identified, and the identification rate of our new algorithm is 91.2%. The experiment results prove that the improved character recognition method is accurate and highly consistent.

A New Recognition Method of Radar Emitter Signal

2013 ◽  
Vol 380-384 ◽  
pp. 3509-3512
Author(s):  
Fei Ye ◽  
Xin Wang ◽  
Xing Rong Gao ◽  
Jun Luo
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Template Matching ◽  
Kernel Density ◽  
Recognition Algorithm ◽  
Radar Signal ◽  
Signal Recognition ◽  
Recognition Method ◽  
Signal Parameters ◽  
Probability Density Curve

According to the problem that the existing radar signal recognition method cannot effectively identify the radar signal, a new recognition method based on kernel density estimation is proposed. First using kernel density estimation gets the probability density curve of radar emitter signal parameters, then storing the cures into database as the characteristics, in the end a radar emitter signal recognition algorithm based on template matching is proposed.

An improved parking space recognition algorithm based on panoramic vision

2021 ◽  
Author(s):  
Jindong Zhang ◽  
Tong Liu ◽  
Xuelong Yin ◽  
Xue Wang ◽  
Kunpeng Zhang ◽  
...  
Keyword(s):  
Recognition Algorithm ◽  
Parking Space ◽  
Panoramic Vision ◽  
Space Recognition
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