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Introducing New AdaBoost Features for Real-Time Vehicle Detection

Abstract : This paper shows how to improve the real-time object detection in complex robotics applications, by exploring new visual features as AdaBoost weak classifiers. These new features are symmetric Haar filters (enforcing global horizontal and vertical symmetry) and N-connexity control points. Experimental evaluation on a car database show that the latter appear to provide the best results for the vehicle-detection problem.
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Contributor : Fabien Moutarde <>
Submitted on : Wednesday, October 7, 2009 - 4:49:48 PM
Last modification on : Thursday, September 24, 2020 - 5:04:01 PM
Long-term archiving on: : Wednesday, June 16, 2010 - 12:28:41 AM


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  • HAL Id : hal-00422587, version 1
  • ARXIV : 0910.1293


Bogdan Stanciulescu, Amaury Breheret, Fabien Moutarde. Introducing New AdaBoost Features for Real-Time Vehicle Detection. COGIS'07 conference on COGnitive systems with Interactive Sensors, Nov 2007, Stanford, Palo Alto, United States. ⟨hal-00422587⟩



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