A real-time oriented system for vehicle detection

Bertozzi, Massimo; Broggi, Alberto; Castelluccio, Stefano

doi:10.1016/s1383-7621(96)00106-3

Cited by 93 publications

(36 citation statements)

References 9 publications

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“…Sun [2] 은 영상을 이용한 차량 인식 시스템은 기본적으로 후 보영역 검출단계와 후보영역에서의 특징 추출 후에 인식 성능을 높이기 위한 검증 단계로 구성된다고 설명한다. 후보 영역 검출을 위한 이전 연구에는 차 량 전면의 밝기 대칭성 [3] 을 이용한 방법, 컬러 정보 [4] 나 밝기 정보의 임계값 이상의 히스토그램을 이용 하여 차량과 배경을 분리하여 검출하는 방법, 코너 점 [6] 이나 수평, 수직 에지성분 [7] 을 이용한 방법, 배 경과 구분되는 차량의 움직임 [8] 이용한 방법, 거리정 보를 이용하는 스테레오 비전 [9,10] 등이 있다. 검증 단계의 연구로는 크게 이미지의 상관관계를 이용하 여 검증을 수행하는 템플릿 기반 방법 [11] 과 신경망 [12] 이나 SVM [13] [14] , 학습 단 계에서 클래스의 구분에 중점을 두는 LDA(Linear Discriminant Analysis) [15] , 통계적 독립성을 이용하 여 독립적인 성분만을 추출하는 ICA(Independent Component Analysis) [16] …”

Section: 수 있게 된다unclassified

Vehicle Recognition using NMF in Urban Scene

Ban¹,

Lee²,

Kang³

2012

The Journal of Korean Institute of Communications and Informati

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The vehicle recognition consists of two steps; the vehicle region detection step and the vehicle identification step based on the feature extracted from the detected region. Features using linear transformations have the effect of dimension reduction as well as represent statistical characteristics, and show the robustness in translation and rotation of objects. Among the linear transformations, the NMF(Non-negative Matrix Factorization) is one of part-based representation. Therefore, we can extract NMF features with sparsity and improve the vehicle recognition rate by the representation of local features of a car as a basis vector. In this paper, we propose a feature extraction using NMF suitable for the vehicle recognition, and verify the recognition rate with it. Also, we compared the vehicle recognition rate for the occluded area using the SNMF(sparse NMF) which has basis vectors with constraint and LVQ2 neural network. We showed that the feature through the proposed NMF is robust in the urban scene where occlusions are frequently occur.주저자：인천대학교 정보통신공학과 영상 연구실,

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Section: 수 있게 된다unclassified

Vehicle Recognition using NMF in Urban Scene

Ban¹,

Lee²,

Kang³

2012

The Journal of Korean Institute of Communications and Informati

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“…If "obstacle" means 1057-7149/98$10.00 © 1998 IEEE a vehicle, then the detection is based on a search for specific patterns, possibly supported by other features, such as shape [56], symmetry [61], or the use of a bounding box [1]. Also, in this case, the processing can be based on the analysis of a single still image.…”

Section: B Obstacle Detectionmentioning

confidence: 99%

GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection

Bertozzi

Broggi

1998

IEEE Trans. on Image Process.

892

404

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Abstract-This paper describes the Generic Obstacle and Lane Detection system (GOLD), a stereo vision-based hardware and software architecture to be used on moving vehicles to increment road safety. Based on a full-custom massively parallel hardware, it allows to detect both generic obstacles (without constraints on symmetry or shape) and the lane position in a structured environment (with painted lane markings) at a rate of 10 Hz. Thanks to a geometrical transform supported by a specific hardware module, the perspective effect is removed from both left and right stereo images; the left is used to detect lane markings with a series of morphological filters, while both remapped stereo images are used for the detection of free-space in front of the vehicle. The output of the processing is displayed on both an onboard monitor and a control-panel to give visual feedbacks to the driver. The system was tested on the mobile laboratory (MOB-LAB) experimental land vehicle, which was driven for more than 3000 km along extra-urban roads and freeways at speeds up to 80 km/h, and demonstrated its robustness with respect to shadows and changing illumination conditions, different road textures, and vehicle movement.

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“…The HG step generally uses one of the following three methods: the knowledge-based method, the stereo vision-based method or the motion-based method. To detect objects and hypothesizes the location of object, the knowledge-based method uses characteristic information such as symmetry [3][4][5][6], corners [7,8], shadows [9], edges [10][11][12], textures [13,14], and vehicle lights [15][16][17]. There are two types of the stereo vision-based method for detecting vehicles.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Vehicle Detection System Applying Hypothesis Fitting

Cheon¹,

Lee²

2017

IJFIS

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In this paper, we propose the improved vision-based vehicle detection system which is added a hypothesis fitting (HF) step to the typical vehicle detection system consisting of the hypothesis generation (HG) and hypothesis verification (HV) step. In the HG step, the system generates hypotheses using shadow regions appearing under vehicles. In the HV step, the system verifies whether a hypothesis is a vehicle or not by applying a classifier and feature vectors extracted from the hypothesis. The proposed HF step is conducted between HG and HV steps and help to improve performance of the vehicle verification by adapting region of hypotheses. We verify the performance of our proposed HF method through a total of 4,797 hypotheses, 1,606 positive hypotheses, and 3,191 negative hypotheses data set.

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A real-time oriented system for vehicle detection

Cited by 93 publications

References 9 publications

Vehicle Recognition using NMF in Urban Scene

Vehicle Recognition using NMF in Urban Scene

GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection

Vision-Based Vehicle Detection System Applying Hypothesis Fitting

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