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Pedestrian detection is one of the most challenging and vital tasks of driver assistance systems (DAS). Among several algorithms developed for human detection, histogram of oriented gradients (HOG) followed by support vector machine (SVM) has shown the most promising results. This paper presents a hardware accelerator for real-time pedestrian detection at different scales to fulfill the real-time...
Speed limit traffic sign recognition plays a key role in intelligent transport system (ITS), especially in driver assistant system (DAS) and intelligent autonomous vehicles (IAV). Although traffic signs are clearly defined in color, shapes for easily detecting purpose, an excellent traffic sign detection system still be a challenge for researchers and manufactures because of the strict requirements...
Robust hand detection and classification is one of the most crucial pre-processing steps to support human computer interaction, driver behavior monitoring, virtual reality, etc. This problem, however, is very challenging due to numerous variations of hand images in real-world scenarios. This work presents a novel approach named Multiple Scale Region-based Fully Convolutional Networks (MSRFCN) to robustly...
Traffic sign detection and recognition systems are essential components of Advanced Driver Assistance Systems and self-driving vehicles. In this contribution we present a vision-based framework which detects and recognizes traffic signs inside the attentional visual field of drivers. This technique takes advantage of the driver 3D absolute gaze point obtained through the combined use of a front-view...
Over the years it has been observed that drowsiness appears as one of the factors of the occurrence of driving accidents. By focusing the study on sleep stage 1, transition period between awakeness and sleepiness, it's possible to create a system capable of detecting drowsiness. In this paper, we describe an electroencephalogram (EEG)-based platform capable of detecting drowsiness. This platform consists...
Distracted driving is the major cause for injuries and fatalities due to road accidents. Driving is a continuous task which requires constant attention of the driver; a certain level of distraction can cause the driver lose his/her attention to the driving task which might lead to an accident. Thus, detection of distraction will help reduce the number of accidents. There has been much research conducted...
In the paper a 79GHz radar located at an intersection is used for classification of pedestrians from vehicles. Compared to in-vehicle radars, radars used for intersection surveillance meet more complicated recognition requirements because of the diversity of moving patterns of both pedestrians and vehicles. We propose to extract a dual set of features from radar measurements and to use them with two...
It is widely known that many traffic accidents occur every year not only in Japan but also throughout the world. Sleepiness or drowsiness, which is the cause of dozing at the wheel, happens regardless of the physical condition of the driver at the time such as after having had meals or at midnight. This indicates that it is too difficult to expect the driver to avoid sleepiness or drowsiness by themselves...
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...
This paper proposed a new Vehicle Make Recognition (VMR) method using the PCANet features extracted from vehicle front view images. The PCANet architecture processes every input vehicle image through only three very simple data processing components: cascaded principle component analysis (PCA), binary hashing, and block-wise histograms, and generates a sparse vector as the feature representation....
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...
Searching through and selecting data sets from large traffic databases with sensor information is often a cumbersome manual process. In this paper we present an idea that may dramatically fasten and streamline this process. The idea is to build a fast search index (COSI: COngestion Search engIne) based on meta data in combination with features from the traffic patterns along routes. Instead of ploughing...
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...
Driver behaviour has a significant influence on vehicle accidents. Measuring and providing feedback on driver behaviour can provide significant benefits for understanding and improving road safety. In order to detect driver actions and driving characteristics from the broadest population of drivers, mobile phones can be used to collect low cost information and provide easy accessibility, using sensors...
We extract 3D curb from video sequence, using a single camera equipped with fish-eye lens and located at the front/rear of the vehicle. The challenge in extracting curbs from images lies in their small size and their lack of texture. We show that by appropriately exploiting appearance features, 3D geometry, and temporal information, one can reliably detect and localize the curbs in the 3D scene. The...
Classification of electroencephalography (EEG)-based application is one of the important process for biomedical engineering. Driver fatigue is a major case of traffic accidents worldwide and considered as a significant problem in recent decades. In this paper, a hybrid deep generic model (DGM)-based support vector machine is proposed for accurate detection of driver fatigue. Traditionally, a probabilistic...
Vehicle Logo Recognition(VLR) has been an important study field in intelligent Transportation system (ITS). This paper proposes to recognize vehicle logo and predict logo attributes by combining Convolutional Neural Network (CNN) with Multi-Task Learning(MTL). In order to accelerate convergence of multi-task model, an adaptive weight training strategy is employed. To verify the algorithm, the Xiamen...
Drowsy driving is a major cause of many traffic accidents. The aim of this work is to develop an automatic drowsiness detection system using an efficient k-nearest neighbors (K-NN) algorithm. First, the distribution of power in time-frequency space was obtained using short-time Fourier transform (STFT) and then, the mean value of power during time-segments of 0.5 second was calculated for each EEG...
We present an efficient approach to lane and pedestrian detection by processing sequential images from a camera attached to a moving vehicle. The left and right lines of the current lane are detected by finding high intensity pixels along multiple horizontal scan lines and connecting the detected pixel points. Line positions are predicted by tracking in order to increase detection credibility while...
We introduce a new computer vision based system for robust traffic sign recognition and tracking. Such a system presents a vital support for driver assistance in an intelligent automotive. Firstly, a color based segmentation method is applied to generate traffic sign candidate regions. Secondly, the HoG features are extracted to encode the detected traffic signs and then generating the feature vector...
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