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Video sensors constitute a great innovation in the automotive sector and road safety as they contribute to the development of driver assistance systems. These video systems use image processing techniques to inform drivers of impending dangers. One such development is the Lane Departure Warning System (LDWS) which play a key role in the prevention from accidents. The main function of this system is...
With rapid digital expansion, usage of digital meters are gaining momentum. However, analog dials are still prevalent in instrument panels due to performance purposes and user appeasement. In teleoperated vehicles and remote monitoring applications there is necessity for processing and wireless transmission of the data collected from the analog gauges. In this paper a methodology for automatic interpretation...
In a diverse country like India, gated communities, corporate premises and university campuses witness a lot of unfamiliar vehicles with number plates in different formats, fonts, font sizes and sometimes even in various languages which enter and leave the estate every day, and it is difficult to register the vehicle numbers manually even for a multi-lingual person. This document aims to extract image...
This article describes the detection of the characters of the license plate through of computer vision techniques: such as cascade of classifiers based in sobel algorithm, analysis of peaks and valleys, and support vector machines; the search for the region of the plate begins by detecting vehicles, then character segmentation and concludes with the recognition of these. The system was tested in different...
In this paper, a shadow detection and removal framework for a video-based traffic monitoring system is proposed. The proposed framework is able to correctly detect if a video sequence has cast shadows that needs to be removed. Shadow detection results in 90.39% shadow detection rate and 88.71% shadow discrimination rate for different daytime traffic scenes with varying shadow strength. A traffic monitoring...
Lane departure and forward collision detection plays an important role in autonomous driving and commercial driver-assistance systems. This paper presents an integrative approach to vision-based lane departure detection which aims to be as simple as possible to enable the real-time computation while being able to adapt to a variety of highway and urban scenarios on different weather conditions. In...
Several vehicle detection methods in urban traffic scenes, such as vehicle detection method based on symmetrical features, vehicle detection method based on license plate, vehicle detection method based on Gabor features and Support Vector Machines (SVM), and vehicle detection method based on Haar-like features and AdaBoost classifier, are comparatively used in this paper. The theoretical analysis...
Computation of Visual Rear Ground Clearance of vehicles is an important computer vision application. This problem is challenging as the road and vehicle rear bumper may have subtle appearance differences, vehicle motion is on uneven surfaces and there are real-time considerations. In this paper a method is presented to compute the Visual Rear Ground Clearance of a vehicle from its rear view video...
This paper describes an efficient method for drowsiness detection by three well defined phases. These three phases are facial features detection using Viola Jones, the eye tracking and yawning detection. Once the face is detected, the system is made illumination invariant by segmenting the skin part alone and considering only the chromatic components to reject most of the non face image backgrounds...
This paper describes a real-time method for building 3D feature models of an object of interest, e.g. a vehicle. The model generation works unsupervised and consists of four recursive steps. The first two steps identify the object of interest and extract the object-related sensor data. In the third and fourth step, prominent features are detected and integrated into a common 3D feature model. This...
Several feature extraction methods, such as the local energy shape histogram, the local binary pattern model and the gradient histogram, are comparatively used to characterize vehicle face images, and Support Vector Machines (SVM) are proposed to classify vehicle brands. Theoretical analysis and experimental results show that the vehicle brand recognition method based on HOG feature extraction and...
In this paper we present a new lane markers detection and estimation algorithm aiming to improve lane detection methods. We first estimate the area of lane marking using the profile of the lane estimation in a confidence map. After that a fitting method is applied to improve the lane marker detection accuracy. To track our lane markers over time and make the association between two iteration, we use...
This paper focuses on the detection of pedestrian crossing intention to improve the situation awareness for autonomous driving in urban environments. A new definition of pedestrian crossing intention is discussed, which allows self-driving vehicles to identify pedestrians, whose intended actions are relevant for the own behavior planning, at an early stage. We propose a context-based feature descriptor...
Real-time video analysis at the edge of the network is very promising to significantly improve public safety, e.g., dangerous accidents detection and find a missing person. Simply uploading the video stream to the cloud for analysis costs too much energy and network bandwidth to an energy-limited camera. Hence we propose EVAPS (Edge Video Analysis for Public Safety), which distributes the computing...
Lane Detection (LD) systems are now commonly used in autonomous cars to assist drivers. However, LD takes up only a small part of the Advanced Driver Assistant Systems (ADAS) and should be highly optimised to make more room for other more complicated algorithms such as stereo vision systems that are incorporated into an ADAS. This paper mainly focuses on an optimised implementation of the linear lane...
In the paper, a near-infrared-ray (NIR) and side-view video based low-complexity drowsy driver detection system is developed for day and night applications. The proposed system detects drowsy conditions effectively whether or not glasses. To reduce the redundant computations, the pre-defined ROI (region of interest) is used at the procedures of face, glasses bridge, eyes, and nose feature detections...
We proposed a Bayesian farmework with the goal to enhance the performance of region proposals generated by Edgeboxes for vehicles. We proposed an innovative objectness measure that combines several geometrical characteristics of proposals into a Bayesian framework. Our method exploits Bayesian to respectively integrate the initial score, aspect ratio and several 3D features related to depth information...
The goal of region proposal approaches is to decrease the hunting zone for classifiers. An innovative objectness measure that combines several characteristics of proposals in a Bayesian framework is explicitly presented in this paper. We try to use Bayesian to respectively integrate four different features of proposals, and employ the posterior probability of positive samples as new score to guide...
Driving behaviour prediction is a challenging problem due to the nonlinearity of human behaviour. Linear and nonlinear techniques have been used to solve this problem, and they provide good results presented in the performance of the current autonomous cars. However, they lack the ability to adapt to abruptness that happens because of the human factor. In this paper, we introduce a method to extract...
This paper considers robust vehicle logo recognition (without aiming at accurate location) for intelligent transportation systems. We propose a recognition-before-location framework for multi-scale vehicle logos which exploits a directional SIFT flow parsing method. We extract dense SIFT descriptors of different standard vehicle logos. An improved matching method is proposed to obtain a directional...
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