A Rear-Vehicle Detection System for Static Images Based on Monocular Vision

Wen, Xuezhi; Zhao, Hong; Wang, Nan; Yuan, Huai

doi:10.1109/icarcv.2006.345157

Cited by 20 publications

(14 citation statements)

References 14 publications

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“…The system complexity was compared with a state of art implementation using Haar wavelet transform and SVM based on [10]. The parameters for comparison were the classifier accuracy and time complexity required for training and testing the same database.…”

Section: Resultsmentioning

confidence: 99%

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Vision based preceding vehicle detection using self shadows and structural edge features

Kanitkar

Bharti

Hivarkar

2011

2011 International Conference on Image Information Processing

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An innovative approach for on-road real-time preceding vehicle detection system is presented in this paper. Vehicle detection is performed by using knowledge based candidate generation followed by appearance based verification. The primary shadow present underneath the vehicle chassis i.e. self shadow is used to generate candidate regions in the image. The use of only self shadow provides improved results and robustness as compared to cast shadows utilized in other approaches.The vehicle class has large intra-class variance due to which a large training dataset with normalized samples is needed for accurate classifier design. It is proposed that the deterministic structure of the contour of vehicles remains same irrespective of its appearance. Hence, structural analysis using the edge based features can be used for classification. It is proposed that a smaller training data-set which is not necessarily normalized is sufficient for good classification results using this analysis. This leads to reduced complexity in system design

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Section: Resultsmentioning

confidence: 99%

“…A model of the road intensity and shadows under the vehicles was used in [9] to estimate the possible presence of vehicles. Wen et al [10] proposed a method to localize the region using the shadows and then classify it using SVM.…”

Section: Introductionmentioning

confidence: 99%

Vision based preceding vehicle detection using self shadows and structural edge features

Kanitkar

Bharti

Hivarkar

2011

2011 International Conference on Image Information Processing

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“…This system includes two modules. The first module aims to segment ROIs accurately according to [30] [31]. The second module, which is the focus of this paper, performs classification on the ROIs.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…SVMs have been widely used for vehicle detection. In [30] and [31], SVM was IEEE Transactions on Circuits and Systems for Video Technology 3 used to classify feature vectors consisting of Haar wavelet coefficients. The combination of HOG features and the SVM classifier has been also used in [32], [33] and [28].…”

Section: B Classificationmentioning

confidence: 99%

“…For a given image, the region of interest (ROI), i.e., the vehicle region, is segmented using shadow, symmetry and aspect ratio information according to [30] [31]. Considering the property of the structure of a vehicle, we add the diagonal features which are described in [46] [47], and the whole Haar-like feature pool we deploy is summarized in Table I.…”

Section: Haar-like Feature Extractionmentioning

confidence: 99%

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Efficient Feature Selection and Classification for Vehicle Detection

Wen

Shao

Fang

et al. 2015

IEEE Trans. Circuits Syst. Video Technol.

140

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Abstract-The focus of this work is on the problem of Haar-like feature selection and classification for vehicle detection. Haar-like features are particularly attractive for vehicle detection because they form a compact representation, encode edge and structural information, capture information from multiple scales, and especially can be computed efficiently. Due to the large-scale nature of the Haar-like feature pool, we present a rapid and effective feature selection method via AdaBoost by combining a sample's feature value with its class label. Our approach is analyzed theoretically and empirically to show its efficiency. Then an improved normalization algorithm for the selected feature values is designed to reduce the intra-class difference while increasing the inter-class variability. Experimental results demonstrate that the proposed approaches not only speed up the feature selection process with AdaBoost but also yield better detection performance than the state-of-the-art methods.

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Vision‐based vehicle detection for road traffic congestion classification

Chetouane

Mabrouk

Jemili

et al. 2020

Concurrency and Computation

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SummaryDue to the increasing number of vehicles in circulation in different urban cities, several automatic traffic monitoring systems have been developed. In particular, traffic monitoring systems using roadside cameras are becoming extensively deployed, as they offer imperative technological advantages compared with other traffic monitoring systems. Vehicle detection and traffic congestion classification are two main steps for video‐based traffic congestion detection systems; the associated methods have a deep impact on the performance of the whole system. In this paper, we investigate four selected vehicle detection methods namely Gaussian Mixture Model (GMM), GMM‐Kalman filter, Optical Flow, and ACF object detector in two contexts: urban and highway. Three traffic congestion classification methods are also studied. The comparative study of the different methods allows us to choose the most appropriate ones to be integrated in the framework proposed to solve the traffic issues in the bridge of Bizerte.

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A Rear-Vehicle Detection System for Static Images Based on Monocular Vision

Cited by 20 publications

References 14 publications

Vision based preceding vehicle detection using self shadows and structural edge features

Vision based preceding vehicle detection using self shadows and structural edge features

Efficient Feature Selection and Classification for Vehicle Detection

Vision‐based vehicle detection for road traffic congestion classification

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