Mohammed Boumediene

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In this communication we present a new algorithm of lane detection and tracking. In the detection step, from the first frame of a video sequence, a linear-parabolic model is used to smooth the estimated trajectories, obtained by using the NNF approach. In the step of tracking, assuming a small change in the model, we use the HMM to update each parameter of(More)
This paper presents an object tracking algorithm using belief functions applied to vision-based traffic sign recognition systems. This algorithm tracks detected sign candidates over time in order to reduce false positives due to data fusion formalization. In the first stage, regions of interest (ROIs) are detected and combined using the transferable belief(More)
This paper describes an efficient method for the detection of triangular traffic signs on grey-scale images. This method is based on the proposed RANSAC symmetric lines detection (RSLD) algorithm which transforms triangle detection into a simple segment detection. A multi-scale approach allows the detection of any warning and yield traffic signs, whatever(More)
This paper tackles the problem of tracking-based Traffic Sign Recognition (TSR) systems. It presents an integrated object detection, association and tracking approach based on a spatio-temporal data fusion. This algorithm tracks detected sign candidates in order to reduce false positives. Regions Of Interest (ROIs) potentially containing traffic signs are(More)
Static and dynamic objects detection and tracking is a classic but still open problem in Intelligent Transportation Systems. Initially formalized in the Bayesian framework, new methods using belief functions have recently emerged. Most of them have been essentially validated in simulations. This paper proposes an association and tracking framework devoted(More)
This paper presents a vehicle detection algorithm for driver assistance system based on embedded vision architecture. Before generating the hypothesis, we propose to delimiter the search area. The Hough transform is used to detect the lines that delimit the search area. By doing so we can reduce the computation time and the false detection rate during the(More)
This paper tackles the object assignment problem in a multiple target tracking context. Multi-target association consists in defining at each time step the relations between two sets. One set is related to the newly detected objects and the other to the already known ones. In this paper, the Dempster-Shafer theory, also known as belief functions is used.(More)
Multiple Object Association (MOA) is an essential process in making vehicles smarter. It consists in associating newly detected objects to previously known ones at each instant in a scene. To treat such matter, the paper proceeds by the use of the Belief Theory introduced by Dempster-Shafer. A crucial concern in this application is the data association(More)
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