Vehicle detection in color images

Rojas, Juan; Crisman, Jill D.

doi:10.1016/s0920-5489(99)91019-x

Cited by 21 publications

(28 citation statements)

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“…The hypothesis generation step uses the knowledge, stereo vision and motion based methods. Knowledge-Based method uses many features (symmetry, color [5], shadow, corner, edge, and texture).…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Method of Vehicle Detection based on Computer Vision for Intelligent Transportation System

Wang¹,

Zhang²

2014

IJMUE

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Section: Related Workmentioning

confidence: 99%

A Hybrid Method of Vehicle Detection based on Computer Vision for Intelligent Transportation System

Wang¹,

Zhang²

2014

IJMUE

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“…The hypothesis generation step uses the knowledge, stereo vision and motion based methods. Knowledge-Based method uses many features (symmetry, color [11], shadow, corner, edge, and texture).Motion-based methods use motion vectors such as optical flow [12] to locate objects with large displacement but such methods suffer from correspondence problems. There are two types of stereo-based methods: disparity map-based and IPM (Inverse Perspective Mapping)-based methods.…”

Section: Introductionmentioning

confidence: 99%

An Application for Stereo Vision based Vehicle/Obstacle Detection for Driver Assistance

SChidanandKumar¹

2013

IJCA

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A stereo vision based vehicle/obstacle detection system has been proposed that generates alarms when vehicles/obstacles are detected in vicinity of near/mid region. Numerous techniques have been applied to detect vehicles/obstacles using a forward facing monocular camera mounted inside a vehicle. This paper presents a methodology for vehicle/obstacle detection using high resolution stereo camera. Stereo cameras are calibrated; stereo images are undistorted and rectified using stereo calibration parameters. Stereo disparity image is then generated using stereo matching algorithm. To localize vehicles/obstacles, search space reduction forms a preliminary step by eliminating sky region retaining only road regions on which vehicles/obstacles are embedded. Periodic peaks in the histogram of stereo disparity image are used as a cue in vehicle/obstacle detection process. Line profiles corresponding to each periodic peak are extracted using vertical strokes. Statistical features are extracted and analyzed for each line profiles to determine the presence/absence of vehicles/obstacles. If vehicles/obstacles are detected in a line profile, then bounding box of blobs are detected using vertical projection technique. Blobs detected from all the gray level profiles are merged together and connected component analysis is applied to count the number of vehicles/obstacles. Color mapping of vehicles/obstacles detected are done to indicate the presence of vehicles/obstacles in near region or mid region thereby generating appropriate alarms.

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“…First, knowledge-based methods use information which we already known such as symmetry [10] , color [11] , shadow [12] and corners [13] information. Second, stereo-based methods take advantage of 3D information such as Inverse Perspective Mapping (IPM) [14] and Disparity Map [15] but it has the shortcoming of high computational cost.…”

Section: Introductionmentioning

confidence: 99%

Fast On-Road Vehicle Detection Using Reduced Multivariate Polynomial Classifier

Kim

Toh

et al. 2012

The Journal of Korea Information and Communications Society

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Vision-based on-road vehicle detection is one of the key techniques in automotive driver assistance systems.However, due to the huge within-class variability in vehicle appearance and environmental changes, it remains a challenging task to develop an accurate and reliable detection system. In general, a vehicle detection system consists of two steps. The candidate locations of vehicles are found in the Hypothesis Generation (HG) step, and the detected locations in the HG step are verified in the Hypothesis Verification (HV) step. Since the final decision is made in the HV step, the HV step is crucial for accurate detection. In this paper, we propose using a reduced multivariate polynomial pattern classifier (RM) for the HV step. Our experimental results show that the RM classifier outperforms the well-known Support Vector Machine (SVM) classifier, particularly in terms of the fast decision speed, which is suitable for real-time implementation. 640 research area aiming to prevent car accidents and reduce the severity of injuries [1] . Generally, a vehicle detection system can be classified into either an active system or a passive system based on the type of sensors used. An active system makes use of active sensors such as laser and radar [19] for imaging. Although the active system can obtain vehicle information directly, it has drawbacks such as low resolution, interference with other active systems, and high cost. On the other hand, a passive system which uses an optical camera is more cost effective than an active system. Moreover, it can be applied to wider applications such as lane detection, traffic sign recognition and object identification than that of an active system. In the passive vehicle detection research, existing vehicle detection systems adopt methods from fields such as pattern classification [7][8] , optical flow [6] , background subtraction [9] and stereo vision [5] . Even though many researchers tried to make robust vehicle detection system through a variety of methods, a credible detection system is yet to be available due to the huge within-class variability in vehicle appearance and environmental variations. There are many kinds of vehicles in terms of appearance such as shape, size and color. Also, complex outdoor environments such as illumination conditions, weather conditions and cluttered backgrounds can be critical for accurate vehicle detection [1] . . The purpose of the HG step is to find out candidate locations of vehicles. After the HG step, the HV step is performed to verify the detected candidate vehicles from the HG step.The HG step can be classified into three categories. First, knowledge-based methods use information which we already known such as symmetry [10] , color [11] , shadow [12] and corners [13] information. Second, stereo-based methods take advantage of 3D information such as Inverse Perspective Mapping (IPM) [14] and Disparity Map [15] but it has the shortcoming of high computational cost. Third, motion-based methods use the information of moving object s...

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Vehicle detection in color images

Cited by 21 publications

References 0 publications

A Hybrid Method of Vehicle Detection based on Computer Vision for Intelligent Transportation System

A Hybrid Method of Vehicle Detection based on Computer Vision for Intelligent Transportation System

An Application for Stereo Vision based Vehicle/Obstacle Detection for Driver Assistance

Fast On-Road Vehicle Detection Using Reduced Multivariate Polynomial Classifier

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