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Real-time visual detection of vehicles and pedestrians with new efficient adaBoost features

Abstract : This paper deals with real-time visual detection, by mono-camera, of objects categories such as cars and pedestrians. We report on improvements that can be obtained for this task, in complex applications such as advanced driving assistance systems, by using new visual features as adaBoost weak classifiers. These new features, the “connected controlpoints” have recently been shown to give very good results on real-time visual rear car detection. We here report on results obtained by applying these new features to a public lateral car images dataset, and a public pedestrian images database. We show that our new features consistently outperform previously published results on these databases, while still operating fast enough for real-time pedestrians and vehicles detection.
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Contributor : Fabien Moutarde Connect in order to contact the contributor
Submitted on : Thursday, September 11, 2008 - 6:44:21 PM
Last modification on : Saturday, October 22, 2022 - 5:20:00 AM
Long-term archiving on: : Friday, June 4, 2010 - 11:16:09 AM


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  • HAL Id : inria-00320888, version 1


Fabien Moutarde, Bogdan Stanciulescu, Amaury Breheret. Real-time visual detection of vehicles and pedestrians with new efficient adaBoost features. 2nd Workshop on Planning, Perception and Navigation for Intelligent Vehicles (PPNIV), at 2008 IEEE International Conference on Intelligent RObots Systems (IROS 2008), Sep 2008, Nice, France. ⟨inria-00320888⟩



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