2014 IEEE Intelligent Vehicles Symposium Proceedings 2014
DOI: 10.1109/ivs.2014.6856490
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Vehicle detection for autonomous parking using a Soft-Cascade AdaBoost classifier

Abstract: Abstract-This paper presents a monocular algorithm for front and rear vehicle detection, developed as part of the FP7 V-Charge project's perception system. The system is made of an AdaBoost classifier with Haar Features Decision Stump. It processes several virtual perspective images, obtained by unwarping 4 monocular fish-eye cameras mounted all-around an autonomous electric car. The target scenario is the automated valet parking, but the presented technique fits well in any general urban and highway environme… Show more

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Cited by 40 publications
(21 citation statements)
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“…In addition to its ease of implementation, AdaBoost is advantageous in that its inner workings are relatively transparent and interpretable. Also, AdaBoost has proved useful in a number of automation-related studies, such as Fan et al and Broggi et al 24,25…”
Section: Bug Detector Implementationmentioning
confidence: 99%
“…In addition to its ease of implementation, AdaBoost is advantageous in that its inner workings are relatively transparent and interpretable. Also, AdaBoost has proved useful in a number of automation-related studies, such as Fan et al and Broggi et al 24,25…”
Section: Bug Detector Implementationmentioning
confidence: 99%
“…A soft-cascade Ada boost classifier with Haar features [1] is trained for vehicle recognition and applied to images to provide frontal and rear vehicle detection. The algorithm which is used takes much time to resolve the image as there is noise captured in the images of the camera.…”
Section: Related Workmentioning
confidence: 99%
“…AVP related advanced automation techniques allow us to extend its functionalities. Here, Long-range AVP (LAVP) became possible due to advancement in fundamental techniques of AVP, like precise detection [5], path generation [6], [7], [8] lateral control [9], cloud-based distributed Vehicle-to-Everything (V2X) architecture [10] and precise localization [2].…”
Section: Introductionmentioning
confidence: 99%