Bassem Besbes

Learn More
Pedestrian detection is an important but challenging component of an Intelligent Transportation System. In this paper, we describe a pedestrian detection system based on a monocular vision with a Far-Infrared camera (FIR). We propose an original feature representation, called Intensity Self Similarity (ISS), adapted to pedestrian detection in FIR images.(More)
One of the main challenges in Intelligent Vehicle is recognition of road obstacles. Our goal is to design a real-time, precise and robust pedestrian recognition system. We choose to use Speeded Up Robust Features (SURF) and a Support Vector Machine (SVM) classifier in order to perform the recognition task. Our main contribution is a method for fast(More)
This paper describes a road obstacle classification system that recognizes both vehicles and pedestrians in far-infrared images. Different local and global features based on Speeded Up Robust Features (SURF) were investigated and then selected in order to extract a discriminative signature from the infrared spectrum. First, local features representing the(More)
In this paper, we present an innovative Augmented Reality prototype designed for industrial education and training applications. The system uses an Optical See-Through HMD integrating a calibrated camera and a laser pointer to interactively augment an industrial object with virtual sequences designed to train a user for specific maintenance tasks. The(More)
One of the main challenges in intelligent vehicles concerns pedestrian detection for driving assistance. Recent experiments have showed that state-of-the-art descriptors provide better performances on the far-infrared (FIR) spectrum than on the visible one, even in daytime conditions, for pedestrian classification. In this paper, we propose a pedestrian(More)
In this work, we focus on an improvement of a road obstacle recognition system using SVM based classifiers combination. The improvement relies on the use of Dempster-Shafer theory (DST) to combine in a finer way the outputs of SVM classifiers. The SVM classifiers were trained on different local and global features based on Speeded Up Robust Features (SURF)(More)
The performance of an object recognition system depends on both object representation and classification algorithms. On the one hand, Object representation by using local descriptors have become a very powerful representation of images. On the other hand, SVM has shown impressive learning and recognition performances. In this paper, we present a method for(More)
This paper addresses the challenging issue of marker less tracking for Augmented Reality. It proposes a real-time camera localization in a partially known environment, i.e. for which a geometric 3D model of one static object in the scene is available. We propose to take benefit from this geometric model to improve the localization of keyframe-based SLAM by(More)
  • 1