Thesis Details
Detekce, sledování a klasifikace automobilů
The aim of this master thesis is to design and implementation in language C++ a system for the detection, tracking and classification of vehicles from streams or records from traffic cameras. The system runs on the platform Robot Operating System and uses the OpenCV, FFmpeg, TensorFlow and Keras libraries. For detection is used cascade classifier, for tracking Kalman filter and for classification of the convolutional neural network. Success rate for detection is 91.93 %, tracking 81.94 % and classification 63.72 %. This system is part of a comprehensive system, that can moreover calibrate video and measure of vehicles speed. The resulting system can be used for traffic analysis.
traffic, detection, tracking, classification, image processing, ROS, cascade classifier, Kalman filter, convolutional neural network
Hrubý Martin, Ing., Ph.D. (DITS FIT BUT), člen
Janoušek Vladimír, doc. Ing., Ph.D. (DITS FIT BUT), člen
Jaroš Jiří, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Matyska Luděk, prof. RNDr., CSc. (FI MUNI), člen
Peringer Petr, Dr. Ing. (DITS FIT BUT), člen
@mastersthesis{FITMT18953, author = "Radek Vop\'{a}lensk\'{y}", type = "Master's thesis", title = "Detekce, sledov\'{a}n\'{i} a klasifikace automobil\r{u}", school = "Brno University of Technology, Faculty of Information Technology", year = 2017, location = "Brno, CZ", language = "czech", url = "https://www.fit.vut.cz/study/thesis/18953/" }