AdaBoost/GA et filtrage particulaire: La vision par ordinateur au service de la sécurité routière

Yotam Abramson
Abstract : This thesis presents two ITS applications,which are designed to be installed on a moving vehicle and detect other road users, using a single frontal camera. The two apllications are Stop and Go ACC and Pedestrian impact prediction. The thesis opens by describing the history and current status of the ITS domain. We review several existing systems which represent several approaches and research directions. Among these systems there are ones which are operational or almost operational, and ones which are futuristic. Next we present some novel results in the field of computer vision/machine learning. Thes results are using, and are partly motivated by, the example of pedestrian detection. In particular we present new type of weak-classifiers to be learned by the AdaBoost algorithm, a classifier which is working faster than others and is not dependant of scene lighting conditions. We also present a novel way to collect large high-quality training sets in order to vastly improve the training results. Using thes results, we present a Stop and Go adaptive cruise control (ACC°. We implemented this application with a set of known image processing algorithms, demonstrating how the combination of several relatively-simple algorithms can yield a reliable system. The application is running in 10 images per second and follows the car in front, while using a motion estimator to detect cut-ins. Our second application is a pedestrian detection and impact prediction application. The system is running in 10 image per second and reliably predict the probability of an impact with a pedestrian in some time frame.
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Submitted on : Friday, March 10, 2006 - 8:00:00 AM
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Yotam Abramson. AdaBoost/GA et filtrage particulaire: La vision par ordinateur au service de la sécurité routière. domain_stic. École Nationale Supérieure des Mines de Paris, 2005. English. ⟨pastel-00001606⟩

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