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Theses

Multiclass object recognition for driving assistance systems and video surveillance

Abstract : Pedestrian Detection and Traffic Sign Recognition (TSR) are important components of an Advanced Driver Assistance System (ADAS). This thesis presents two methods for eliminating false alarms in pedestrian detection applications and a novel three stage approach for TSR. Our TSR approch consists of a color segmentation, a shape detection and a content classification phase. The red color enhancement is improved by using an adaptive threshold. The performance of the K-d tree is augmented by introducing a spatial weighting. The Random Forests yield a classification accuracy of 97% on the German Traffic Sign Recognition Benchmark. Moreover, the processing and memory requirements are reduced by employing a feature space reduction. The classifiers attain an equally high classification rate using only a fraction of the feature dimension, selected using the Random Forest or Fisher's Criterion. This technique is also validated on two different multiclass benchmarks: ETH80 and Caltech 101. Further, in a static camera video surveillance application, the immobile false positives, such as trees and poles, are eliminated using the correlation measure over several frames. The recurring false alarms in the pedestrian detection in the scope of an embedded ADAS application are removed using a complementary tree filter.
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https://pastel.archives-ouvertes.fr/pastel-00657727
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Submitted on : Monday, January 9, 2012 - 10:22:19 AM
Last modification on : Wednesday, October 14, 2020 - 3:52:36 AM
Long-term archiving on: : Tuesday, April 10, 2012 - 2:22:46 AM

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  • HAL Id : pastel-00657727, version 1

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Fatin Zaklouta. Multiclass object recognition for driving assistance systems and video surveillance. Automatic. École Nationale Supérieure des Mines de Paris, 2011. English. ⟨NNT : 2011ENMP0045⟩. ⟨pastel-00657727⟩

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