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In this paper we focus on the problem of pedestrian detection in low visibility conditions, with infrared cameras. Widely applied, tracking is essential for driving assistance applications, providing support for removing false positives and forcing the detection of border line true positives. We propose a multiple feature and temporal based pedestrian detector for far-infrared images. Our model benefits...
In this paper, we tackle the challenge of developing a stereovision-based driving assistance system for collision avoidance on a mobile device. This novel system is aimed to work in an urban environment, where a driver experiences lots of stop and go situations. In cities, rear-end accidents are the most common type of accidents, since a momentary lapse of concentration can lead to unsafe headway...
Autonomous Vehicles require accurate environment information in order to perform scene understanding with very high accuracy. For this reason, we must design perception algorithms that are robust and must perform in any environmental and weather conditions. Since fog is one of the most dangerous weather phenomena for driving we propose an embedded solution that is able to restore the contrast of images...
This article presents a new methodology of enriching digital maps in order to reason upon the traffic sign compliance, of traffic participants. The Open Street Map (OSM) data is augmented with a new layer for road orientation and road intersections in order to support not only map matching, but also reasoning processes on the compliance to traffic regulations provided by traffic signs. In order to...
An important cause of road accidents is the reduced visibility due to the presence of fog or haze. For this reason, there is a fundamental need for Advanced Driving Assistance Systems (ADAS) based on efficient real time algorithms able to detect the presence of fog, estimate the fog's density, determine the visibility distance and inform the driver about the maximum speed that the vehicle should be...
Stereo cameras are a viable solution for reconstructing 3D scenes and are well suited for advanced driver assistance systems, autonomous driving and robotics applications. Modern stereo reconstruction algorithms offer good results, but require very much memory and their real time capabilities are limited on modern day processors. On the other hand, local window aggregation algorithms have a small...
This paper presents a cooperative advanced driving assistance application for lane change maneuvers. The application is deployed on a smart mobile device equipped with a stereo imaging system, that implements the image processing tasks, and performs the fuzzy logic reasoning to asses the safety of the maneuver. Since cooperative driving assistance systems have gained extreme importance in the last...
The images captured in fog conditions have degraded contrast, which makes current image processing applications sensitive and error prone. We propose in this paper an efficient single-image enhancement algorithm suitable for daytime fog conditions and, based on an original mathematical model, for computing the atmospheric veil, taking into account the variation in fog density to the distance. This...
The images captured in fog conditions have degraded contrast, that makes current image processing applications sensitive and error prone. We propose in this paper an efficient single image enhancement algorithm suitable for daytime fog conditions and based on an original mathematical model, for computing the atmospheric veil, that takes into account the variation in fog density to the distance. This...
Fog is one of the most dangerous weather phenomena in driving scenarios that can benefit from driving assistance systems. For this reason we have developed an affordable advanced driving assistance solution that runs on Android based smart mobile platforms. Our system is able to detect the presence of fog in images captured from a moving vehicle, to estimate the fog's density, compute the visibility...
An advanced driving assistance system (ADAS) must also take into account the weather conditions. One of the most dangerous weather conditions for driving scenarios is the presence of fog. So an important task for a driving assistance system is to detect the presence of fog, estimate the fog's density and determine the visibility distance of the driver. Our method is based on a single in-vehicle camera...
Modeling the performance of large scale systems is the core idea of this paper.We focus on modeling the performance specific behavior of LarKC 1- The Large Knowledge Collider a platform for large scale integrated reasoning and Web-search. A set of instrumentation and monitoring tools are employed to collect metrics related to execution time, resources, and specific platform measurements like running...
This paper presents a method for the detection and localization of stop-lines and other horizontal road markings as bicycle or pedestrian crossings encountered in road environments. The few existing stop-line detection approaches found in literature are based on monocular vision, and therefore cannot infer high accuracy information from the visual data. The proposed solution uses a hybrid approach...
The quality of images in real time vision tends to be of extreme importance. Without high quality images processing of the outside world can introduce extreme errors. Image quality is dependent on many environmental factors, such as weather and lighting conditions (sun, rain, snow, fog, mist), entering and going out of tunnels, shadows, cars headlights. This paper presents a new approach for adapting...
In the last years the urban road traffic became overcrowded, requiring higher vigilance from the driver. In order to ensure a better road safety, the car manufacturers compete to offer obstacle detection and representation functions for driver assistance in difficult situations. The fast processing speed and reduced power consumption offered by FPGA devices help overcome the issue of real-time obstacle...
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