Evaluation of MoG Video Segmentation on GPU-based HPC System

Automated and intelligent video surveillance systems play an important role in the modern world. Since the number of various video streams that must be analyzed concurrently grows, such systems can assist humans in performing tiresome tasks. In order to be effective, video surveillance systems have to meet several requirements: they must be accurate and able to process the received video stream in real-time. A robust system should not depend on lighting conditions, illumination changes and other sources of scene variation. A common component of surveillance systems is a module that performs background estimation and foreground segmentation. The MoG (Mixture of Gaussians) algorithm is a widely used statistical technique of video segmentation. The estimation process is time-consuming, especially for complex mixture models containing many components. The work presented here focuses on the performance evaluation of MoG algorithm aiming to assess feasibility of OpenCL-based processing of high resolution video on GPU accelerated platforms

Image Processing Using FPGAs

This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs

Jahresbericht 2014 / Institut für Angewandte Informatik (KIT Scientific Reports ; 7697)

Im Jahresbericht 2014 des Instituts für Angewandte Informatik werden, nach einem kurzen Überblick über die Arbeiten, die Forschungsergebnisse dieses Jahres vorgestellt, die Einordnung erfolgt entsprechend der Zuordnung der Vorhaben zu den Programmen des Großforschungsbereichs des KIT. Es schließt sich ein Verzeichnis der im Berichtszeitraum erschienenen Publikationen des Instituts an

CIRA annual report FY 2017/2018

Reporting period April 1, 2017-March 31, 2018