A vehicle detection system based on Haar and Triangle features

Haselhoff, Anselm; Kummert, Anton

doi:10.1109/ivs.2009.5164288

Cited by 56 publications

(33 citation statements)

References 8 publications

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“…Haar features are sensitive to vertical, horizontal, and symmetric structures [9]. In [108], Haar and Triangle features were proposed for vehicle detection systems. HOG and Haar features were used to detect vehicle in [109].…”

Section: A Vehicle Detectionmentioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

297

125

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Abstract-This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area.Index Terms-Intelligent vehicles, environment perception and modeling, lane and road detection, traffic sign recognition, vehicle tracking and behavior analysis, scene understanding.

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Section: A Vehicle Detectionmentioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

297

125

View full text Add to dashboard Cite

show abstract

“…In [36], vehicle detection was performed with a combination of triangular and rectangular features. In [34], a similar combination of rectangular and triangular features was used for vehicle detection and tracking, using Adaboost classification.…”

Section: B Vehicle Detection and Trackingmentioning

confidence: 99%

Integrated Lane and Vehicle Detection, Localization, and Tracking: A Synergistic Approach

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

163

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Abstract-In this paper, we introduce a synergistic approach to integrated lane and vehicle tracking for driver assistance. The approach presented in this paper results in a final system that improves on the performance of both lane tracking and vehicle tracking modules. Further, the presented approach introduces a novel approach to localizing and tracking other vehicles on the road with respect to lane position, which provides information on higher contextual relevance that neither the lane tracker nor vehicle tracker can provide by itself. Improvements in lane tracking and vehicle tracking have been extensively quantified. Integrated system performance has been validated on real-world highway data. Without specific hardware and software optimizations, the fully implemented system runs at near-real-time speeds of 11 frames per second.

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“…(1) Haar Feature -Haar-like features are popular image features used in object recognition by [36] [2] [17] which are similar to Haar wavelets used in the first real-time face detector by Viola et al [42]. One other contribution was to use summed area which they called integral images.…”

Section: Feature Selectionmentioning

confidence: 99%

Multiple Kernel based KNN Classifiers for Vehicle Classification

Mishra

Banerjee²

2013

IJCA

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The problem of vehicle classification has been addressed in this correspondence. Vehicle classification is a difficult task due to near similarity among various types vehicle features. Spectral properties of the image and near similarity between the front side of different vehicles makes the generalization process even more difficult. Here a multiple kernel based k-nearest neighbor classifier has been designed to improve the classification accuracy. After extracting the frames from the traffic video, vehicles are detected using background subtraction method. Then a wavelet and interest point based feature extraction step is carried out for each detected vehicle. Final classification is carried out using the newly proposed multiple kernel based k-nearest neighbor( KNN) algorithm. Experiments on several real time data-sets establish the higher accuracy of the proposed method in comparison to three well-known state of the art classification techniques.

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A vehicle detection system based on Haar and Triangle features

Cited by 56 publications

References 8 publications

Overview of Environment Perception for Intelligent Vehicles

Overview of Environment Perception for Intelligent Vehicles

Integrated Lane and Vehicle Detection, Localization, and Tracking: A Synergistic Approach

Multiple Kernel based KNN Classifiers for Vehicle Classification

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