|
|
On-Board License Plate Detection and Recognition
Tomovič, Martin ; Sochor, Jakub (referee) ; Špaňhel, Jakub (advisor)
This Bachelor's thesis aims to create an aplication for detection and recognition of license plates suitable for real-time processing. The work contains analysis of available methods. Part of the work is focused on present form of licence plates in Czech Republic. As a result of work, new data set was created and computer application was implemented. The application uses existing libraries designed for computer vision and machine learning with main purpose to detect and recognize licence plates from video. Detection is realized with help of cascade classifier, and recognition by Perceptron neural network. Final chapter subsequently contains evaluation of success rate of implemented solution.
|
|
|
Section Speed Measurement for Traffic Analysis
Kubíčková, Pavla ; Špaňhel, Jakub (referee) ; Sochor, Jakub (advisor)
This bachelor thesis focuses on section speed measurement for traffic analysis. This thesis desribes existing methods of detection of license plates and classification of their characters. Methods of cascade classifier and classifier SVM are described in this work. Evaluation of individual parts of the system is processed in the final section.
|
|
|
Movement Analysis of Vehicles on Crossroads
Benček, Vladimír ; Juránek, Roman (referee) ; Sochor, Jakub (advisor)
This thesis proposes and implements a system for movement analysis of vehicles on crossroads. It detects and tracks the movement of vehicles in the video, gained from the stationary video camera, which has the view of some crossroad. The trajectories are stored and their number and directions are analysed. The detection was made using cascade classifier. A dataset of 10500 positive and 10500 negative samples has been created to train the classifier. Vehicles are tracked using KCF method. For trajectory clustering, needed by analysis, the Mean Shift method is used. Testing showed, that the overall success of vehicle movement analysis is 92.77%.
|
|
|
Computer Application Control by Natural Head Movement
Vojvoda, Jakub ; Materna, Zdeněk (referee) ; Beran, Vítězslav (advisor)
The aim of this work is to design and implement a system that tracks user's head in the input video frames and on the basis of its position achieves interaction with computer applications. Four methods for head detection have been proposed based on computer vision techniques as face detection by Haar-like features, background detection, Camshift or Lucas-Kanade to calculate an optical flow. The individual methods have been tested on recorded and in this field used data sets, and evaluated. The implementation of the system was then used to build the demo applications.
|
|
|
Detection and Recognition of Traffic Signs in Image
Spáčil, Pavel ; Hradiš, Michal (referee) ; Herout, Adam (advisor)
This work focuses on classification and recognition of traffic signs in image. It describes briefly some used methods a deeply describes chosen system including extensions and method for creating models needed for classification. There's described implementation of library and demonstration program including important pieces of knowledge discovered during development. There're also results of some experiments and possible enhancements in conclusion.
|
|
|
License Plate Detection and Recognition for Traffic Analysis
Černá, Tereza ; Hradiš, Michal (referee) ; Herout, Adam (advisor)
This thesis describes the design and development of a system for detection and recognition of license plates. The work is divided into three basic parts: licence plates detection, finding of character positions and optical character recognition. To fullfill the goal of this work, a new dataset was taken. It contains 2814 license plates used for training classifiers and 2620 plates to evaluate the success rate of the system. Cascade Classifier was used to train detector of licence plates, which has success rate up to 97.8 %. After that, pozitions of individual characters were searched in detected pozitions of licence plates. If there was no character found, detected pozition was not the licence plate. Success rate of licence plates detection with all the characters found is up to 88.5 %. Character recognition is performed by SVM classifier. The system detects successfully with no errors up to 97.7 % of all licence plates.
|
guest ::
