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Autonomous vehicles are very hot research area which advance artificial intelligent, pattern recognition, computer vision, sensor fusion and control theory. In the paper we demonstrate a kind of micro-autonomous vehicle, describing hardware and software architectures, perception system and control method-cloud control. Our goal is study interaction behaviors between autonomous vehicles and how to...
Computer vision techniques applied to systems used on road maintenance, which are related either to traffic signs or to the road itself, are playing a major role in many countries because of the higher investment on public works of this kind. These systems are able to collect a wide range of information automatically and quickly, with the aim of improving road safety. In this context, the correct...
In this paper, we address the problem of vehicle detection and tracking with low-angle cameras by combining windshield detection and feature points clustering, effectively fusing several primitive image features such as color, edge and interest point. By exploring various heterogenous features and multiple vehicle models, we achieve at least two improvements over the existing methods: higher detection...
The future of intelligent vehicles will rely on robust information to allow the proper feedback to the vehicle itself, to issue several kinds of active safety, but before all, to generate information for the driver by calling his or her attention to potential instantaneous or mid-term risks associated with the driving. Before true vehicle autonomy, safety and driver assistance are a priority. Sophisticated...
The recognition and tracking of traffic lights for intelligent vehicles based on a vehicle-mounted camera are studied in this paper. The candidate region of the traffic light is extracted using the threshold segmentation method and the morphological operation. Then, the recognition algorithm of the traffic light based on machine learning is employed. To avoid false negatives and tracking loss, the...
We propose a new traffic analysis framework using existing traffic camera networks. The framework integrates vehicle detection and image-based matching methods with geographic context to match vehicles across different views and analyze traffic. This is a challenging problem due to the low frame-rate of traffic-cams and the large distance between views. A vehicle may not always appear in a camera...
Due to the recent progress in computer vision to interpret images and sequence of images, the video camera is a promising sensor for traffic monitoring and traffic surveillance at low cost. This paper focuses on the detection and tracking of multiple vehicles present in the field of view of a camera. Until now, the vehicle detection has been mainly performed by the widely used technique called background...
Accurate lane detection in real-time is a critical task in autonomous vehicle guidance and lane departure warning for driver assistance. Existing vision-based approaches rely mostly on some analysis of the spatial gradient of the image. However, if the road structure is not regular and well delimited, edges may not be easy to extract and other features must be employed. This paper evaluates the use...
In modern intelligent transportation systems, the video image vehicle detection system (VIVDS) is gradually becoming one of the popular methods at signalized traffic intersection due to its convenient installation and rich information content provided. However, in the current VIVDS, the camera usually is installed at the roadside poles or traffic light poles, which not only requires more than one...
This paper presents methods for vision-based detection and tracking of vehicles in monocular image sequences of traffic scenes recorded by a stationary camera. The goal of this research is to develop suitable methods for automatic visual traffic surveillance to perform detection, tracking and traffic parameter estimation of multiple vehicles in real time as well as tackle environment illumination...
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