GPU Accelerated Number Plate Localization in Crowded Situation

Number Plate Localization (NPL) has been widely used as part of Automatic Number Plate Recognition (ANPR) system. NPL method determines the accuracy of ANPR system. Although it is a mature research, the challenge stills persist especially in crowded situation where many vehicles present. Therefore, a method is proposed to localize number plate in crowded situation. The proposed NPL method uses vertical edge density to extract potential region of number plate then detect the number plate using combination of Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM). The method employs GPU to deal with multiple number plate detection, to handle multi-scale detection window, and to perform real time detection. The test result shows good results, 0.9883 value of AUC (Area Under Curve), and 0.9362 of BAC (Balance Accuracy). Moreover, potential real time detection is foreseen because total process is executed in less than 50 ms. Errors are mainly caused by background that contain letters, non-standard number plate and highly covered number plat

An Accelerated Hierarchical Approach for Object Shape Extraction and Recognition

We present a novel automatic supervised object recognition algorithm based on a scale and rotation invariant Fourier descriptors algorithm. The algorithm is hierarchical in nature to capture the inherent intra-contour spatial relationships between the parent and child contours of an object. A set of distance metrics are introduced to go along with the hierarchical model. To test the algorithm, a diverse database of shapes is created and used to train standard classification algorithms, for shape-labeling. The implemented algorithm takes advantage of the multi-threaded architecture and GPU efficient image-processing functions present in OpenCV wherever possible, speeding up the running time and making it efficient for use in real-time applications. The technique is successfully tested on common traffic and road signs of real-world images, with excellent overall performance that is robust to moderate noise levels

A Survey of Techniques for Improving Security of GPUs

Graphics processing unit (GPU), although a powerful performance-booster, also has many security vulnerabilities. Due to these, the GPU can act as a safe-haven for stealthy malware and the weakest `link' in the security `chain'. In this paper, we present a survey of techniques for analyzing and improving GPU security. We classify the works on key attributes to highlight their similarities and differences. More than informing users and researchers about GPU security techniques, this survey aims to increase their awareness about GPU security vulnerabilities and potential countermeasures

GPU-based pedestrian detection for autonomous driving

Pedestrian detection has gained a lot of prominence during the last few years. Besides the fact that it is one of the hardest tasks within computer vision, it involves huge computational costs. Obtaining acceptable real-time performance, measured in frames per second (fps), for the most advanced algorithms is nowadays a hard challenge. In this work, we propose a GPU implementation of a well-known pedestrian detection system (i.e., HOGLBP-SVM) specially designed for the Tegra X1 embedded GPU. It includes LBP and HOG as feature descriptors and SVM as classifiers. We introduce significant algorithmic adjustments and optimizations to adapt the problem to the NVIDIA GPU architecture without sacrificing accuracy. The aim of this work is to offer a real-time system providing reliable results.La detecció de vianants ha estat un tema de molt interès els darrers anys. A part de ser una de les tasques més complexes de la visió per computador, implica uns costos computacionals molt elevats. Obtenir un rendiment de temps real acceptable, mesurat en imatges processades per segon (fps), per la majoria d'algoritmes més avançats és una fita complicada. Aquest treball proposa una implementació en GPU d'un conegut detector de vianants (i.e., HOGLBP-SVM) dissenyat expressament per la Tegra X1, una GPU encastada. El detector inclou els mètodes LBP i HOG com descriptors de característiques i un SVM com a classificador. El sistema introdueix ajustos algorítmics i optimitzacions per adaptar el problema a l'arquitectura d'una GPU NVIDIA sense sacrificar precisió. L'objectiu és proporcionar un sistema de temps real que alhora sigui robust.La detección de peatones ha ganado mucho interés en los últimos años. A parte de ser una de las tareas más complejas dentro la visión por computador, esta implica unos costes computacionales muy elevados. Obtener un rendimiento de tiempo real aceptable, medido en imágenes procesadas por segundo (fps), para la mayoría de algoritmos más avanzados es un hito complicado. Este trabajo propone una implementación en GPU de un conocido detector de peatones (i.e., HOGLBP-SVM) diseñado para la Tegra X1, una GPU embebida. El detector incluye los metodos LBP i HOG como descriptores de características i un SVM como clasificador. El sistema introduce ajustes algorítmicos i optimizaciones para adaptar el problema a la arquitectura de una GPU NVIDIA sin sacrificar precisión. El objetivo es proporcionar un sistema de tiempo real que a la vez sea robusto

A survey of intrusion detection system technologies

This paper provides an overview of IDS types and how they work as well as configuration considerations and issues that affect them. Advanced methods of increasing the performance of an IDS are explored such as specification based IDS for protecting Supervisory Control And Data Acquisition (SCADA) and Cloud networks. Also by providing a review of varied studies ranging from issues in configuration and specific problems to custom techniques and cutting edge studies a reference can be provided to others interested in learning about and developing IDS solutions. Intrusion Detection is an area of much required study to provide solutions to satisfy evolving services and networks and systems that support them. This paper aims to be a reference for IDS technologies other researchers and developers interested in the field of intrusion detection