Measurement of Road Traffic Parameters Based on Multi-Vehicle Tracking

Development of computing power and cheap video cameras enabled today's traffic management systems to include more cameras and computer vision applications for transportation system monitoring and control. Combined with image processing algorithms cameras are used as sensors to measure road traffic parameters like flow volume, origin-destination matrices, classify vehicles, etc. In this paper we propose a system for measurement of road traffic parameters (basic motion model parameters and macro-scopic traffic parameters). The system is based on Local Binary Pattern (LBP) image features classification with a cascade of Gentle Adaboost (GAB) classifiers to determine vehicle existence and its location in an image. Additionally, vehicle tracking and counting in a road traffic video is performed by using Extended Kalman Filter (EKF) and virtual markers. The newly proposed system is compared with a system based on background subtraction. Comparison is performed by the means of execution time and accuracy.Comment: Part of the Proceedings of the Croatian Computer Vision Workshop, CCVW 2015, Year

Hardware Accelerated Digital Image Stabilization in a Video Stream

Cílem této práce je návrh nové techniky pro stabilizaci obrazu za pomoci hardwarové akcelerace prostřednictvím GPGPU. Využití této techniky umožnuje stabilizaci videosekvencí v reálném čase i pro video ve vysokém rozlišení. Toho je zapotřebí pro ulehčení dalšího zpracování v počítačovém vidění nebo v armádních aplikacích. Z důvodu existence vícerých programovacích modelů pro GPGPU je navrhnutý stabilizační algoritmus implementován ve třech nejpoužívanějších z nich. Jejich výkon a výsledky jsou následně porovnány a diskutovány.The aim of this thesis is to propose a new method for digital image stabilization in video stream by exploiting computing power of GPGPU. This unit enables a real time stabilization of high resolution digital video sequences, which is important for further post-processing in computer vision and/or military applications. In order to compare available architectures for GPGPU programming, the proposed algorithm is implemented in three major frameworks. Results are then compared and discussed.