Dissertation > Excellent graduate degree dissertation topics show

Detection and Recognition of Road Traffic Signs

Author: YuYingYing
Tutor: DuanShan
School: Central South University for Nationalities
Course: Mathematics and Applied Mathematics
Keywords: traffic signs detection threshold segmentation geometrical features analysis statistics slope change SIFT A-SIFT
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 116
Quote: 2
Read: Download Dissertation

Abstract


With the development of urbanization, intelligent transportation system is one of important methods to solve traffic jams. Intelligent Transportation System(ITS) is an integrated system combining the technology of communication, measurement control and computer. With the development of intelligent transportation systems, "intelligent vehicles (unmanned vehicles)"also came into being. Conversely, intelligent vehicle research also contributed to the growing maturity of the development of intelligent transport systems.Computer vision-based traffic sign recognition under the natural scene is the key to intelligent vehicle technology and one of the difficulties. Although many experts at home and abroad have spent a lot of years to study, the problem is still not a good solution for this situation. In this paper, traffic sign detection and recognition have been researched in the following aspects:(1).We collected the traffic signs of natural scenes are RGB color space image. According to the color characteristics of traffic signs, the image in RGB space was analyzed and found that the difference between 3 channels to maintain a certain range, therefore, according to this feature through the appropriate threshold to achieve good results.(2) In this paper, the first step for further analysis of the segmented image, image segmentation that occurs after a lot of noise, according to the subsequent recognition as a great obstacle. Analysis showed that the size of traffic signs in a certain range, the paper together with the region to fill and the area threshold method to remove a large area and small area of the noise points. Further observed features of traffic signs, traffic signs can easily find the shape of the geometry are the rules, for example, the ban signs, its shape is round, from a mathematical point of view, showing circular boundary of the slope of the characteristics of a certain regularity. This article features the shape of traffic signs through the proposed changes based on statistics of tangent shape of traffic signs detection algorithm to accurately locate traffic signs.(3) Traffic sign recognition will be detected to determine what kind of traffic signs for the process. Traffic sign recognition has many ways, for example: template matching algorithm, cluster analysis, artificial neural networks. The latest research at home and abroad is based on SIFT-matching recognition algorithm. SIFT-matching algorithm which can overcome the image of the rotation, size, and the effect of light is considered nowadays the most popular method. This article is based on SIFT algorithm was proposed based on A-SIFT matching algorithm for traffic sign recognition.Experimental results show that the traffic sign recognition algorithm has better robustness.

Related Dissertations

  1. The Research of Color Image Watermarking Algorithm Against Geometric Attacks,TP309.7
  2. The Research of Image Matching Method Based on Feature Descriptor,TP391.41
  3. Research on Key Technologies for Binocular Stereo Vision,TP391.41
  4. The Research on Mammographic Images Registration,TP391.41
  5. A Self-calibration Method of Camera Based on Binocular Stereo Vision,TP391.41
  6. The Research of Real-time Based Width Measurement Technology Based on Binocular Stereo Panoramic Vision Sensor,TP274
  7. Research on Technology of Terrain Measuring Based on Binocular Vision,TP391.41
  8. Research on Local Feature Based Image Copy Detection,TP391.41
  9. Research on Target Tracking Algorithms Based on Particle Filter Method,TP391.41
  10. Research on Algorithmof Target Detection and Tracking in Traffic Surveillance Video,TP391.41
  11. The Research of Phishing Detection Technology Based on Nearest Neighbor and Similarity Measurement,TP393.08
  12. The Structural Model Learning Based on Local Features and the Application in Object Detection and Localization,TP391.41
  13. Supervised Learning Based Image Local Interest Points Detector,TP391.41
  14. GPU-based SIFT algorithm,TP391.41
  15. SIFT-based digital watermarking algorithm,TP309.7
  16. BoW-SIFT -based model and the level of the three-dimensional mesh feature retrieval system,TP391.41
  17. Based on the geometry of multiple images and textures automatically rebuild,TP391.41
  18. Saliency -based image retrieval keying Research and Implementation,TP391.41
  19. SVM Based on SIFT and scene classification,TP391.41
  20. Image registration based on the rotation angle calculation methods sand,TP391.41
  21. Research on Video Processing Technology of Airborne Optical Measurement System,TP391.41

CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
© 2012 www.DissertationTopic.Net  Mobile