Model based vehicle detection for intelligent vehicles

Collado, J.M.; Hilario, Cristina; Escalera, Arturo de la; Armingol, José María

doi:10.1109/ivs.2004.1336447

Cited by 44 publications

(34 citation statements)

References 13 publications

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“…In a previous research, [15], the seven parameters had a range but, while the range of the X and Y position, and the width and height of th e vehicle were i n pixels, t he ran ge of t he wi ndshield and bumper position a nd t he ro of a ngle were a percentage of the height or width. A previous detection of the road limits is done in [2] [10].…”

Section: Geometrical Model Of a Vehiclementioning

confidence: 89%

Multi-resolution Image Analysis for Vehicle Detection

Hilario

Collado

Armingol

et al. 2005

Pattern Recognition and Image Analysis

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Abstract.Computer Vision can provide a great deal of assistance to Intelligent Vehicles. In this paper an Advanced Driver Assistance Systems for Vehicle Detection is presen ted. A ge ometric model of th e vehicle is defin ed where its energy function in cludes infor mation of the shape and symmetry of the vehicle and the shadow it produces. A genetic algorithm finds the optimum parameter values. As the algorithm receiv es info rmation from a road detection modul e some geometric restrictions can be applied. A multi-resolution approach is used to speed up th e algorithm and w ork in re altime. Examples of re al im ages ar e shown to validate the algorithm.

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Section: Geometrical Model Of a Vehiclementioning

confidence: 89%

Multi-resolution Image Analysis for Vehicle Detection

Hilario

Collado

Armingol

et al. 2005

Pattern Recognition and Image Analysis

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“…Consequently, more variability is found in the gradient orientation map, and therefore more bins are necessary to capture fine-detail. A good trade-off between complexity and performance is achieved by selecting (b, n) = (2,8) for the close/middle and far ranges, and (b, n) = (3,12) for the left and right views. This involves respective detection accuracies of 94.88, 85.92, 91.82, and 89.42%, which results in an average correct detection rate of 90.51%.…”

Section: Experiments On Vehicle Detectionmentioning

confidence: 99%

“…The former exploit a priori knowledge of the structure of the vehicles to generate a description (i.e., the model) that can be matched with the hypotheses to decide whether they are vehicles or not. Both rigid (e.g., [7]) and deformable (e.g., [8]) vehicle models have been proposed. Appearance-based techniques, in contrast, involve a training stage in which features are extracted from a set of positive and negative samples to design a classifier.…”

Section: Introductionmentioning

confidence: 99%

Video analysis-based vehicle detection and tracking using an MCMC sampling framework

Arróspide

Salgado

Nieto

2012

EURASIP J. Adv. Signal Process.

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This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimensionality of the feature space grows, which creates significant limitations when tracking multiple objects. Alternatively, the proposed method is based on a Markov chain Monte Carlo (MCMC) approach, which allows efficient sampling of the feature space. The method involves important contributions in both the motion and the observation models of the tracker. Indeed, as opposed to particle filter-based tracking methods in the literature, which typically resort to observation models based on appearance or template matching, in this study a likelihood model that combines appearance analysis with information from motion parallax is introduced. Regarding the motion model, a new interaction treatment is defined based on Markov random fields (MRF) that allows for the handling of possible inter-dependencies in vehicle trajectories. As for vehicle detection, the method relies on a supervised classification stage using support vector machines (SVM). The contribution in this field is twofold. First, a new descriptor based on the analysis of gradient orientations in concentric rectangles is defined. This descriptor involves a much smaller feature space compared to traditional descriptors, which are too costly for real-time applications. Second, a new vehicle image database is generated to train the SVM and made public. The proposed vehicle detection and tracking method is proven to outperform existing methods and to successfully handle challenging situations in the test sequences.

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“…Vision-based vehicle detection method mainly consists of vehicle models [1], the optical flow methods [2] and feature methods [3]. In [4], Zeng zhihong established a 'U' model and a rectangular model, which were used to detect the long distance and short distance vehicle, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…In [4], Zeng zhihong established a 'U' model and a rectangular model, which were used to detect the long distance and short distance vehicle, respectively. A fine vehicle model, which has seven parameters, was built according to the features of car body [5] to search and match of vehicles by using genetic algorithms and energy function. Model-based vehicle detection has a great dependent on model, so that this scheme is not good for real-time detection.…”

Section: Introductionmentioning

confidence: 99%

Multiple Features Fusion for Front-View Vehicle Detection

Men¹,

Dai²

2015

Advances in Intelligent Systems Research

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Abstract-With the rapid increase of vehicle holdings, urban transport is facing a severe test. Driver-assistance systems (ADAS) can effectively avoid happening of traffic accidents. Real-time detection of moving vehicle based on vision has become a research focus of ADAS. A method of multiple features fusion for front-view vehicle detection is proposed in this paper. Firstly, in RGB color space, license plate position is located using color conversion and Otsu threshold segmentation method is improved to confirm the vehicle's candidate area. Secondly, geometrical characteristics of the license plate is adopted to eliminate distraction regions and to verify the extracted license plate. Finally, rear lamps of vehicle are detected and matched in candidate license plate regions, so vehicle area is further determined. The experimental results show that the proposed method in this paper can be used to detect vehicle in real-time and false detection rate is low. Keywords-rear-view vehicle; RGB color space; Otsu threshold segmentation I INTRODUCTIONDriver-assistance systems require real-time and reliably to detect vehicle and pedestrian around the vehicle driving direction, to remind the driver or vehicle to automatically take corresponding measures to avoid potential dangers. In ADAS, front-view vehicle detection is one of the ways to improve security, it can effectively avoid the rear-end collisions of traffic accidents due to lack of safe distance between vehicles.Vision-based vehicle detection method mainly consists of vehicle models [1], the optical flow methods [2] and feature methods [3]. In [4], Zeng zhihong established a 'U' model and a rectangular model, which were used to detect the long distance and short distance vehicle, respectively. A fine vehicle model, which has seven parameters, was built according to the features of car body [5] to search and match of vehicles by using genetic algorithms and energy function. Model-based vehicle detection has a great dependent on model, so that this scheme is not good for real-time detection. Vehicle detection based on optical flow can not detect slow or stationary vehicle. Feature-based vehicle detection method using significant features of vehicle, such as color, edge symmetry and vehicle's shadow, to segment vehicle area [6,7]. When there is a large impact on image from surroundings light and noise, single feature detection will be weakened, so multi-feature combination is able to improve the accuracy of vehicle detection.Detecting the candidate area of vehicle based on color feature is a popular method. Cheng et al.[8] used a new model for the color conversion, transformed the three color components to two components, one of which represents the color model of vehicle, and the other represents a color model of non-vehicle, the method is appropriate for detecting aerial images, which reduce the effect on billboards and buildings of the road. Yan et al. [9] made binary image processing by setting H and S components threshold, this man-made threshold does not have un...

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Model based vehicle detection for intelligent vehicles

Cited by 44 publications

References 13 publications

Multi-resolution Image Analysis for Vehicle Detection

Multi-resolution Image Analysis for Vehicle Detection

Video analysis-based vehicle detection and tracking using an MCMC sampling framework

Multiple Features Fusion for Front-View Vehicle Detection

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