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It is estimated that 80% of crashes and 65% of near collisions involved drivers inattentive to traffic for three seconds before the event. This paper develops an algorithm for extracting characteristics allowing the cell phones identification used during driving a vehicle. Experiments were performed on sets of images with 100 positive images (with phone) and the other 100 negative images (no phone),...
Driver drowsiness may cause traffic injuries and death. In literature, various methods, for instance, image-based, vehicle-based, and biometric-signals-based, have been proposed for driver drowsiness detection. In this paper, a new approach using Electrocardiogram is discussed. Performance evaluation is carried out for the driver drowsiness classifier. The developed classifier yields overall accuracy,...
Support Vector Machine (SVM) classifier with Histogram of Oriented Gradients (HOG) feature become one of the most popular techniques used for vehicle detection in recent years. And the computing time of SVM is a main obstacle to get real time implementation which is important for Advanced Driver Assistance Systems (ADAS) applications. One of the effective ways to reduce the computing complexity of...
This paper presents a popular method called boosted hog features to detect pedestrians and vehicles in static images. We compared the differences and similarities of detecting pedestrians and vehicles, then we selected boosted hog features to get an satisfying result. In the part of detecting pedestrians, Histograms of Oriented Gradients (HOG) feature is applied as the basic feature due to its good...
This paper presents an improved vision-based algorithm for detecting and recognizing vehicle logos in images captured by road surveillance cameras. Vehicle logo recognition is quite a challenging task considering the low resolution of the logos, the wide range of variability in illumination and the interference of the air-intake grille. However, our system, assessed on a set of 1386 vehicle images...
High accuracy pedestrian detection plays an important role in all intelligent vehicles. This paper describes a system for detecting the obstacles in front of the vehicle and classifying them in pedestrians and non-pedestrians. It acquires the traffic scenes using a low-cost pair of gray intensities stereo cameras. A SORT-SGM stereo-reconstruction technique is used in order to obtain high density and...
In order to recognize multi-class vehicles, traditional methods are typically based on license plates and frontal images of vehicles. These methods rely heavily on specific datasets and thus are not applicable in real-world tasks. In this paper, we propose a novel method based on a hierarchical model, HMAX, which simulates visual architecture of primates for object recognition. It can extract features...
Drowsiness is one of the main causes of severe traffic accidents occurring in our daily life. In order to reduce the number of drowsiness-induced accidents, various researches have been conducted with the aim of finding practical and non-invasive drowsiness detection systems by using behavioral measuring techniques. Many of the previous works on behavioral measuring techniques have mainly focused...
We propose a linear dimensionality reduction method, Composite Discriminant Factor (CDF) analysis, which searches for a discriminative but compact feature subspace that can be used as input to classifiers that suffer from problems such as multi-collinearity or the curse of dimensionality. The subspace selected by CDF maximizes the performance of the entire classification pipeline, and is chosen from...
Vehicle recognition is a challenging task with many useful applications. State-of-the-art methods usually learn discriminative classifiers for different vehicle categories or different viewpoint angles, but little work has explored vehicle recognition using semantic visual attributes. In this paper, we propose a novel iterative multiple instance learning method to model local attributes and viewpoint...
This paper presents a comparative study of several classification methods for the task of recognizing traffic signs in urban areas. These classification methods are artificial neural network (ANN), k-nearest neighbors (kNN), support vector machine (SVM), and random forest (RF). First, HSI-based color segmentation process is applied to obtain candidate regions. Using centroid-based feature, these regions...
We cast vehicle recognition as problem of feature representation and classification, and introduce a sparse learning based framework for vehicle recognition and classification in this paper. After objects captured with a GMM background subtraction program, images are labeled with vehicle type for dictionary learning and decompose the images with sparse coding (SC), a linear SVM trained with the SC...
Classification of vehicle logo is an important step towards the vehicle recognition that is required in many applications in intelligent transportation systems and automatic surveillance. A fast and reliable vehicle logo classification approach is proposed by first accurate logo detection, followed by an improved local-mean based classification algorithm. The recently published integrative logo detection...
Intelligent Transportation System is a worldwide research hotspot and the extraction of traffic parameters is a crucial part of it for subsequent identification of traffic states. This paper proposes a novel approach of extracting traffic parameters such as time occupancy, volume and vehicle velocity based on video images. Visual features obtained from spatio-temporal images are more immune to environmental...
The recognition of vehicle manufacturer logo is a crucial and very challenging problem, which is still an area with few published effective methods. This paper proposes a new fast and reliable system for Vehicle Logo Recognition (VLR) based on Bag-of-Words (BoW). In our system, vehicle logo images are represented as histograms of visual words and classified by SVM in three steps: firstly, extract...
This paper proposes a vehicle size classification system which distinguishes small-size cars, medium-size cars, and big-size cars automatically. Previous vehicle size classification researches usually fixed the camera viewpoint or limited it to small orientations. The proposed system utilizes the concavity property of sedans and buses to distinguish small-size and big-size cars in large orientations...
This paper focuses on monocular-video-based stationary detection of the pedestrian's intention to enter the traffic lane. We propose a motion contour image based HOG-like descriptor, MCHOG, and a machine learning algorithm that reaches the decision at an accuracy of 99 % within the initial step at the curb of smart infrastructure. MCHOG implicitly comprises the body language of gait initiation, especially...
In this paper, we propose a new traffic surveillance system with the ability to perform surveillance tasks in real time. The proposed classification method is able to classify objects into vehicles and non-vehicles (pedestrians and motorcycles). In addition, the system can detect the type of vehicle as large or small efficiently, without considering size-based features. Our tracking algorithm uses...
To improve automotive active safety and guarantee the safety of pedestrians at night time, a fast pedestrian detection method based on monocular far-infrared camera for driver assistance systems is proposed. According to the distribution of gray-level intensity of pedestrian samples, an adaptive local dual threshold segmentation algorithm is executed first to extract candidate regions. The presented...
In this paper, we focus on a challenging pattern recognition problem of significant industrial impact: classifying vehicles from their rear videos as observed by a camera mounted on top of a highway with vehicles traveling at high speed. To solve this problem, we present a novel feature called structural signatures. From a rear view video, a structural signature recovers the vehicle side profile information...
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