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Weapon Detection in an Image
Debnár, Pavol ; Drahanský, Martin (referee) ; Dvořák, Michal (advisor)
This thesis is focused on the topic of firearms detection in images. In the theoretic section, the explanation of the term firearm is covered, along with the definition of the most prevalent firearm categories. Then the concept of image noise and the ways it can hinder image detection is covered, along with ways of reducing it. Next, algorithms of image detection are introduced - first those which operate on the basis of neural nets - such as Convolutional Neural Nets and Single Shot Multibox Detection. The next section discusses classic algorithms of object detection such as HOG+SVM and SURF. After that, information on the used libraries and software is provided. The experimental part covers the designed algorithm and database. For detection, the HOG+SVM, SURF and SSD algorithms were used. All the algorithms are tested on the database and, if possible, on video. A final evaluation is provided, along with possible future development options.
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Pedestrians Tracking in a Video Record from a Stationary Camera
Trnkal, Milan ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
This bachelor thesis focuses on pedestrians tracking from camera. In this work, I have introduced several methods of computer vision suitable for detection and classification of people. I proposed an algorithm for detecting and tracking pedestrians based on detection of movement. The application uses a Histogram of Oriented Gradients and SVM classifier together with color histograms for identification of pedestrians. Pedestrian's trajectories are then rendered to the output. Last part of the thesis deals with testing and evaluation of the results of the algorithm.
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Crowd Counting in Video
Kuřátko, Jiří ; Sochor, Jakub (referee) ; Herout, Adam (advisor)
This master's thesis prepared the programme which is able to follow the trajectories of the movement of people and based on this to create various statistics. In practice it is an effective marketing tool which can be used for instance for customer flow analyses, optimal evaluation of opening hours, visitor traffic analyses and for a lot of other benefits. Histograms of oriented gradients, SVM classificator and optical flow monitoring were used to solve this problem. The method of multiple hypothesis tracking was selected for the association data. The system's quality was evaluated from the video footage of the street with the large concentration of pedestrians and from the school's camera system, where the movement in the corridor was monitored and the number of people counted.
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Moving Objects Detection in Video Sequences
Němec, Jiří ; Herout, Adam (referee) ; Španěl, Michal (advisor)
This thesis deals with methods for the detection of people and tracking objects in video sequences. An application for detection and tracking of players in video recordings of sport activities, e.g. hockey or basketball matches, is proposed and implemented. The designed application uses the combination of histograms of oriented gradients and classification based on SVM (Support Vector Machines) for detecting players in the picture. Moreover, a particle filter is used for tracking detected players. The whole system was fully tested and the results are shown in the graphs and tables with verbal descriptions.
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Traffic signs detection and recognition
Dvořák, Michal ; Richter, Miloslav (referee) ; Honec, Peter (advisor)
The goal of this thesis is the utilization of computer vision methods, in a way that will lead to detection and identification of traffic signs in an image. The final application is to analyze video feed from a video camcorder placed in a vehicle. With focus placed on effective utilization of computer resources in order to achieve real time identification of signs in a video stream.
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Face Recognition
Keršner, Martin ; Mlích, Jozef (referee) ; Juránek, Roman (advisor)
The thesis deals with Face Recognition. The aim was to study the various methods of feature extraction and determine their influence on the success of recognition. The methods of feature extraction include the Local Binary Pattern, Histogram Of Oriented Gradients and Gabor Filter. Face recognition of image similarity will be described. Support Vectore Machines was used in the experiments. Experimentally determined parameters of the most successful methods were used in the system for simple Face Recognition.
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Moving Objects Detection in Video Sequences
Němec, Jiří ; Herout, Adam (referee) ; Španěl, Michal (advisor)
This thesis deals with methods for the detection of people and tracking objects in video sequences. An application for detection and tracking of players in video recordings of sport activities, e.g. hockey or basketball matches, is proposed and implemented. The designed application uses the combination of histograms of oriented gradients and classification based on SVM (Support Vector Machines) for detecting players in the picture. Moreover, a particle filter is used for tracking detected players. The whole system was fully tested and the results are shown in the graphs and tables with verbal descriptions.
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Comparison of Classification Methods
Dočekal, Martin ; Zendulka, Jaroslav (referee) ; Burgetová, Ivana (advisor)
This thesis deals with a comparison of classification methods. At first, these classification methods based on machine learning are described, then a classifier comparison system is designed and implemented. This thesis also describes some classification tasks and datasets on which the designed system will be tested. The evaluation of classification tasks is done according to standard metrics. In this thesis is presented design and implementation of a classifier that is based on the principle of evolutionary algorithms.
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Weapon Detection in an Image
Debnár, Pavol ; Drahanský, Martin (referee) ; Dvořák, Michal (advisor)
This thesis is focused on the topic of firearms detection in images. In the theoretic section, the explanation of the term firearm is covered, along with the definition of the most prevalent firearm categories. Then the concept of image noise and the ways it can hinder image detection is covered, along with ways of reducing it. Next, algorithms of image detection are introduced - first those which operate on the basis of neural nets - such as Convolutional Neural Nets and Single Shot Multibox Detection. The next section discusses classic algorithms of object detection such as HOG+SVM and SURF. After that, information on the used libraries and software is provided. The experimental part covers the designed algorithm and database. For detection, the HOG+SVM, SURF and SSD algorithms were used. All the algorithms are tested on the database and, if possible, on video. A final evaluation is provided, along with possible future development options.
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