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Vehicle and Pedestrian Detection is a key problem in computer vision, with several applications including robotics, surveillance and automotive safety. Much of the progress of the past few years has been driven by the availability of challenging public datasets. In this paper, we build up a vehicle and pedestrian detection system by combing Histogram of Oriented Gradients (HoG) feature and support...
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...
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...
In using image analysis to assist a driver to avoid obstacles on the road, traditional approaches rely on various detectors designed to detect different types of objects. We propose a framework that is different from traditional approaches in that it focuses on finding a clear path ahead. We assume that the video camera is calibrated offline (with known intrinsic and extrinsic parameters) and vehicle...
Existing pedestrian and vehicle detection algorithms use 2D cues of objects, such as pixel values, color, texture, shape information or motion. The use of 3D cues in object detection, on the other hand, is not well studied in the literature. In this paper, we propose an efficient algorithm that detects pedestrian and vehicle using their 3D cues. The proposed algorithm first detects moving objects...
In this paper, we present a two-stage vision-based approach to detect front and rear vehicle views in road scene images using eigenspace and a support vector machine for classification. The first stage is hypothesis generation (HG), in which potential vehicles are hypothesized. During the hypothesis generation step, we use a vertical, horizontal edge map to create potential regions where vehicles...
Because vehicles moving over ground can generate a succession of impacts on the earth's magnetic field, we can detect them by means of detecting magnetic perturbation using a magnetic sensor, and automatically recognize them by advanced signal processing and recognition method. Comparing to traditional devices, magnetic sensors fabricated with Micro-electro-mechanical system (MEMS) technology is a...
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