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Detection and classification of vehicles are the most challenging tasks of a video-based intelligent transportation system. Traditional detection and classification methods are based on subtraction of estimated still backgrounds from a video to find out the moving objects. In general, these methods are computationally highly expensive, and in many cases show poor detection and classification performance,...
Intelligent airbag system can control its deploy time and force according to different types of occupant in different sitting position. The accurate detection of vehicle occupant is the precondition and plays an important role in such system. This paper presents a vision detection method using low-cost CMOS camera and pattern recognition algorithm for the classification of different occupant classes...
For an autonomous vehicle, detecting and tracking other vehicles is a critical task. Determining the orientation of a detected vehicle is necessary for assessing whether the vehicle is a potential hazard. If a detected vehicle is moving, the orientation can be inferred from its trajectory, but if the vehicle is stationary, the orientation must be determined directly. In this paper, we focus on vision-based...
In this paper, a general algorithm for pedestrian detection by on-board monocular camera which can be applied to cameras of various view ranges in unified manner. The Spatio-Temporal MRF model extracts and tracks foreground objects as pedestrians and non-pedestrian distinguishing from background scenes as buildings by referring to motion difference. During the tracking sequences, cascaded HOG classifiers...
Road detection is an important problem with application to driver assistance systems and autonomous, self-guided vehicles. The focus of this paper is on the problem of feature extraction and classification for front-view road detection. Specifically, we propose using Support Vector Machines (SVM) for road detection and effective approach for self-supervised online learning. The proposed road detection...
This paper presents a set of algorithms for vehicle detection in large scale aerial images. Vehicles are detected based on geometric and radiometric features, extracted within a multiresolution linear Gaussian scale-space. The image features, described by their local structures, are classified using support vector machines. Classified features are then clustered by an unsupervised affine propagation...
This paper describes the comparison of accuracy and performance of two machine learning approaches for visual object detection and tracking vehicles, from an on-road image sequence. The first is a neural network based approach. where an algorithm of multi resolution technique based on Haar basis functions was used to obtain an image with different scales. Thereafter a classification was carried out...
The need for a generic and adaptable object detection and recognition method in images, is becoming a necessity today, given the rapid development of the internet and multimedia databases in general. This paper compares the state-of-the-art in object recognition and proposes a method based on adaptable models for detecting thematic categories of objects. Furthermore, automatically constructed semantics...
This paper presents a vision based scheme for detecting flying vehicle using a new feature extraction and correspondence algorithm as well as a motion flow vectors classifier. The base of detection is to classify the motion flow vectors of object and scene at two video sequences from a mobile monocular CCD camera. For this purpose, we introduce a method to extract robust features from fuzzified edges...
Trained detectors are the most popular algorithms for the detection of vehicles or pedestrians in video sequences. To speed up the processing time the trained stages build a cascade of classifiers. Thereby the classifiers become more powerful from stage to stage. The most popular classifier for real-time applications is Adaboost applied to rectangular Haar-like features. The processing time of these...
Reliable detection and classification of vulnerable road users constitute a critical issue on safety/protection systems for intelligent vehicles driving in urban zones. In this subject, most of the perception systems have LIDAR and/or radar as primary detection modules and vision-based systems for object classification. This work, on the other hand, presents a valuable analysis of pedestrian detection...
This study presents an effective system for detecting and tracking moving vehicles in nighttime traffic scene for traffic surveillance. The proposed method identifies vehicles based on detecting and locating vehicle headlights and taillights by using the techniques of image segmentation and pattern analysis. First, to effectively extract bright objects of interest, a fast bright-object segmentation...
For an intelligent transportation system (ITS), traffic incident detection is one of the most important issues, especially for urban area which is full of signaled intersections. In this paper, we propose a novel traffic incident detection method based on the image signal processing and hidden Markov model (HMM) classifier. First, a traffic surveillance system was set up at a typical intersection...
Detecting vehicles from video sequence is very challenging due to the wide varieties of vehicle appearances and the complexity of the backgrounds. At present, many algorithms in the image recognition have a narrow applicability and a weak real-time. Aiming at this problem, a recognition method which was combined by features extraction using Gabor wavelet and BP neural network algorithm for the classification...
In many driver assistance systems and autonomous driving applications, both LIDAR and computer vision (CV) sensors are often used to detect vehicles. LIDAR provides excellent range information to different objects. However, it is difficult to recognize these objects as vehicles from range information alone. On the other hand, computer vision imagery allows for better recognition, but does not provide...
In this paper, the framework is presented for using active learning to train a robust monocular on-road vehicle detector for active safety, based on Adaboost classification and Haar-like rectangular image features. An initial vehicle detector was trained using Adaboost and Haar-like rectangular image features and was very susceptible to false positives. This detector was run on an independent highway...
In recent years, the Viola and Jones rapid object detection approach became very popular. One aspect why this approach achieved acceptance is the numerical efficient computation of the Haar-like features on basis of the integral image. This efficiency is essential for sliding window techniques, where features have to be extracted for huge amounts of data. The main contribution of this paper is an...
Surveillance system involving hundreds of cameras becomes very popular. Due to various positions and orientations of camera, object appearance changes dramatically in different scenes. Traditional appearance based object classification methods tend to fail under these situations. We approach the problem by designing an adaptive object classification framework which automatically adjust to different...
We present a new multi-stage algorithm for car and truck detection from a moving vehicle. The algorithm performs a search for pertinent features in three dimensions, guided by a ground plane and lane boundary estimation sub-system, and assembles these features into vehicle hypotheses. A number of classifiers are applied to the hypotheses in order to remove false detections. Quantitative analysis on...
An object-oriented image analysis method has been developed to detect, classify and count road vehicles from airborne color digital orthoimagery. The basic difference, especially when compared with previously developed pixel-based vehicle detection procedures, is that we don't process and analyze image pixels, but rather image objects that are extracted from image segmentation. We aim to characterize...
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