Hardware Architecture Design for Vehicle Detection Using a Stereo Camera

Jungdong Jin,Dongkyun Kim,Ji Ho Song,Vinh Dinh Nguyen,Jae Wook Jeon 제어로봇시스템학회 2011 제어로봇시스템학회 국제학술대회 논문집 Vol.2011 No.10

Vehicle detection systems are increasingly important in providing vehicle road safety. However, many problems remain, such as a limit in the number of vehicles detected, false vehicle detection, and large processing time. Thus, we propose an efficient method for vehicle detection. This paper simulates vehicle detection using a rectified image and a disparity image based on dedicated hardware architecture. Every vehicle detection function, including vehicle corner detection, vehicle region expansion, and vehicle region validation, is implemented using parallel architecture. Our proposed hardware architecture is suitable for real time processing using the dedicated hardware architecture. In addition, we can detect many vehicles in the rectified image and reduce the false detection of vehicles.

A Novel Particle Filter Implementation for a Multiple-Vehicle Detection and Tracking System using Tail Light Segmentation

Ming Qing,조강현 제어·로봇·시스템학회 2013 International Journal of Control, Automation, and Vol.11 No.3

This paper proposes a vision-based multiple vehicle automatic detection and tracking system which can be applied in different environments. To detect vehicles, tail light position is utilized for fast vehicle candidate localization. A back propagation neural network (BPNN) trained by a Gabor feature set is used. BPNN verifies vehicle candidates and ensures detection system robustness. In the vehicle tracking step, to overcome multiple vehicle tracking challenges, partial vehicle occlusion and temporarily missing vehicle problems, this paper propose a novel method implementing a particle filter. The color probability distribution function (CPDF) of detected vehicles is used twice in the vehicle tracking sub-system. Firstly, CPDF is adopted to seek potential target vehicle locations; secondly, CPDF is used to measure the similarity of each particle for target vehicle position estimation. Because of various illuminations or target vehicle distances, the same vehicle will generate different CPDFs; the initial CPDF cannot guarantee long-term different scale vehicle tracking. To overcome these problems, an accurate tracking result, which is chosen by a trained BPNN, is used to update target vehicle CPDF. In our experiments, the proposed algorithm showed 84% accuracy in vehicle detection. Videos col-lected from highways, urban roads and campuses are tested in our system. The system performance makes it appropriate for real applications.

통계적 특징 기반 SVM을 이용한 야간 전방 차량 검출 기법

정정은,김현구,박주현,정호열,Joung, Jung-Eun,Kim, Hyun-Koo,Park, Ju-Hyun,Jung, Ho-Youl 대한임베디드공학회 2012 대한임베디드공학회논문지 Vol.7 No.4

A driver assistance system is critical to improve a convenience and stability of vehicle driving. Several systems have been already commercialized such as adaptive cruise control system and forward collision warning system. Efficient vehicle detection is very important to improve such driver assistance systems. Most existing vehicle detection systems are based on a radar system, which measures distance between a host and leading (or oncoming) vehicles under various weather conditions. However, it requires high deployment cost and complexity overload when there are many vehicles. A camera based vehicle detection technique is also good alternative method because of low cost and simple implementation. In general, night time vehicle detection is more complicated than day time vehicle detection, because it is much more difficult to distinguish the vehicle's features such as outline and color under the dim environment. This paper proposes a method to detect vehicles at night time using analysis of a captured color space with reduction of reflection and other light sources in images. Four colors spaces, namely RGB, YCbCr, normalized RGB and Ruta-RGB, are compared each other and evaluated. A suboptimal threshold value is determined by Otsu algorithm and applied to extract candidates of taillights of leading vehicles. Statistical features such as mean, variance, skewness, kurtosis, and entropy are extracted from the candidate regions and used as feature vector for SVM(Support Vector Machine) classifier. According to our simulation results, the proposed statistical feature based SVM provides relatively high performances of leading vehicle detection with various distances in variable nighttime environments.

Vision-based Vehicle Detection and Inter-Vehicle Distance Estimation

Giseok Kim,Jae-Soo Cho 제어로봇시스템학회 2012 제어로봇시스템학회 국제학술대회 논문집 Vol.2012 No.10

In this paper, we propose a vision-based robust vehicle detection and inter-vehicle distance estimation algorithm for driving assistance system. It uses the directional edge features, as well as the Haar-like features of car rear-shadows for detection of front vehicles. The use of additional vehicle edge features greatly reduces the false-positive errors. And, after analyzing two inter-vehicle distance estimation methods: the vehicle position-based and the vehicle width-based algorithm, a novel improved inter-vehicle distance estimation algorithm that uses the advantage of both methods is proposed. Various experimental results show the effectiveness of the proposed method.

On-road Vehicle Detection based on Appearance Features for Autonomous Vehicles

Tae-Young Lee,Jae-Saek Oh,Jung-Ha Kim 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10

In this paper, we propose a monocular camera-based vehicle detection system for use in autonomous vehicles. In order to accurately and rapidly detect a vehicle on the real road, we have designed a vehicle detection system that follows two basic steps namely; Hypothesis Generation and Hypothesis Verification. In the hypothesis generation step, a candidate region of vehicles is set by using the shadow properties of the vehicle. In the hypothesis verification step, based on the candidate regions, we are able to distinguish between the vehicle and the non-vehicle. For the hypothesis verification, we use histograms of oriented gradients (HOG) feature and support vector machine (SVM) classifier. To fit the vehicle detection system, detailed settings of the HOG such as the cell, block and bin were selected.

스테레오 비전기반 차량 감지 시스템의 개발

황준연(Junyeon Hwang),허건수(Kunsoo Huh) 한국자동차공학회 2007 한국자동차공학회 Symposium Vol.- No.-

Vehicle detection is a crucial issue for driver assistance as well as for autonomous vehicle guidance function and it has to be performed with high reliability to avoid any potential collision. The vision-based vehicle detection systems are regarded promising for this purpose because they require little infrastructure on a highway. However, the feasibility of these systems in passenger car requires accurate and robust sensing performance. In this paper, vehicles detection system using stereo vision sensors is developed. This system utilizes morphological filter, feature detector, template matching and epipolar constraint techniques in order to detect the corresponding pairs of the vehicles. After the initial detection, the system executes the tracking algorithm for the vehicles. The proposed system can detect front vehicles, a leading vehicle and side lane vehicles. The position parameters of the vehicles located forward can be obtained. The proposed vehicle detection system is implemented on a passenger car and its performance is verified experimentally.

불법 주정차 차량 단속을 위한 차량 검지 및 추적 기법

김승균,김효각,장동니,박상희,고성제,Kim, Seung-Kyun,Kim, Hyo-Kak,Zhang, Dongni,Park, Sang-Hee,Ko, Sung-Jea 한국전기전자학회 2009 전기전자학회논문지 Vol.13 No.2

본 논문은 불법 주정차 단속을 위한 정지 차량 검지 및 추적 기법을 제안한다. 제안하는 알고리즘은 크게 네 부분으로 구성되어 있다. 먼저, 입력 영상으로부터 움직이는 차량을 구분하기 위하여 향상된 코드북 물체 검지 알고리즘을 이용한 차량 검지 알고리즘을 제안한다. 두 번째로 차량의 기하학적 특징을 이용하여 차량이 아닌 물체는 제외시키는 전처리 기법을 사용한다. 그런 다음, 검지된 결과 차량들을 히스토그램 추적 기법과 칼만 필터를 결합한 추적 알고리즘을 이용하여 추적한다. 추적 결과를 더 정확하게 하기 위하여, 히스토그램 추적 결과를 칼만 필터의 측정 데이터로 사용한다. 마지막으로, 정지 차량 검지 알고리즘의 신뢰성 있고 정확한 성능을 위하여 실제 정지 카운터 (RSC)를 제안한다. 실험결과로부터 제안한 시스템은 복잡한 실제 도로 환경에서도 여러 차량을 동시에 추적할 수 있고, 정지 차량을 성공적으로 검지해냄을 확인한다. This paper presents a robust vehicle detection and tracking algorithm for supervision of illegal parking. The proposed algorithm is composed of four parts. First, a vehicle detection algorithm is proposed using the improved codebook object detection algorithm to segment moving vehicles from the input sequence. Second, a preprocessing technique using the geometric characteristics of vehicles is employed to exclude non-vehicle objects. Then, the detected vehicles are tracked by an object tracker which incorporates histogram tracking method with Kalman filter. To make the tracking results more accurate, histogram tracking results are used as measurement data for Kalman filter. Finally, Real Stop Counter (RSC) is introduced for trustworthy and accurate performance of the stopped vehicle detection. Experimental results show that the proposed algorithm can track multiple vehicles simultaneously and detect stopped vehicles successfully in the complicated street environment.

On-Road Succeeding Vehicle Detection using Characteristic Visual Features

Shyam Prasad Adhikari(샴아디카리),Hitek Cho(조휘택),Hyeon-Joong Yoo(유현중),Changju Yang(양창주),Hyongsuk Kim(김형석) 대한전기학회 2010 전기학회논문지 Vol.59 No.3

A method for the detection of on-road succeeding vehicles using visual characteristic features like horizontal edges, shadow, symmetry and intensity is proposed. The proposed method uses the prominent horizontal edges along with the shadow under the vehicle to generate an initial estimate of the vehicle-road surface contact. Fast symmetry detection, utilizing the edge pixels, is then performed to detect the presence of vertically symmetric object, possibly vehicle, in the region above the initially estimated vehicle-road surface contact. A window defined by the horizontal and the vertical line obtained from above along with local perspective information provides a narrow region for the final search of the vehicle. A bounding box around the vehicle is extracted from the horizontal edges, symmetry histogram and a proposed squared difference of intensity measure. Experiments have been performed on natural traffic scenes obtained from a camera mounted on the side view mirror of a host vehicle demonstrate good and reliable performance of the proposed method.

“Moving Object Tracking of Vehicle Detection” : A Concise Review

보안공학연구지원센터(IJSIP) 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.3

Vehicle detection and tracking applications play an important role for military and civilian applications such as in highway traffic surveillance control management and traffic planning.This paper presents a review on the various techniques of On-Road Vehicle detection systems that are based on motion model. In this paper a literature Survey of previous and recent works is presented on vision-based vehicle detection using sensors. Detecting the objects in the video and tracking their motion to identify their characteristics has been emerging as a demanding research area in the domain of Image Processing and Computer Vision. The traffic image analysis comprises of three parts: (1) Traffic Analysis (2) Motion Vehicle Detection and Segmentation Approaches and (3) Vehicle Tracking Approaches. In this survey, we have classified these methods into various groups, and these groups are providing a detailed description of various representation methods and find out their positive and negative aspects.

Fast Vehicle Detection using Correlation Filters

Sangpil Han,Min-jae Kim,Seokmok Park,Joonki Paik 대한전자공학회 2017 IEIE Transactions on Smart Processing & Computing Vol.6 No.5

Object detection is very challenging research in the computer vision community, and vehicle detection has become an important issue in various applications, such as unmanned systems and intelligent transportation systems. Most of these applications require fast and accurate vehicle detection. In recent years, it has been proven that correlation filters can find target objects fast and accurately owing to Parseval’s theorem and dense sampling. However, we think that the existing correlation filters have not used all the helpful information. Therefore, we propose a robust and fast vehicle detection method based on an improved correlation filter framework that exploits the additional information from correlation filters. The proposed vehicle detection algorithm consists of five steps: i) training the correlation filter, ii) correlation of input and the trained filter, iii) finding local maxima as vehicle candidates in the correlation output, iv) filtering the candidates by using the shape and sharpness of the maxima, and v) estimation of the location and scale of the vehicles. The proposed algorithm runs fast and accurately, so it can be applied to many other applications, such as object alignment, object detection, and object tracking. We evaluated the proposed algorithm performance by comparing it with the state-of-the-art correlation filter-based methods.