Thesis Details
Detekce palných zbraní v obrazu
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.
firearms, object detection, SURF, SSD, HOG, SVM, Speeded-Up Robust Features, Single Shot Multibox Detector, Histogram of Oriented Gradients, Support Vector Machines, firearm detection, neural nets, convolutional neural nets, CNN
Bartík Vladimír, Ing., Ph.D. (DIFS FIT BUT), člen
Bařina David, Ing., Ph.D. (DCGM FIT BUT), člen
Jaroš Jiří, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Kočí Radek, Ing., Ph.D. (DITS FIT BUT), člen
@bachelorsthesis{FITBT21743, author = "Pavol Debn\'{a}r", type = "Bachelor's thesis", title = "Detekce paln\'{y}ch zbran\'{i} v obrazu", school = "Brno University of Technology, Faculty of Information Technology", year = 2019, location = "Brno, CZ", language = "czech", url = "https://www.fit.vut.cz/study/thesis/21743/" }