An improved "flies" method for stereo vision: application to pedestrian detection

In the vast research field of intelligent transportation systems, the problem of detection (and recognition) of environment objects, for example pedestrians and vehicles, is indispensable but challenging. The research work presented in this paper is devoted to stereo-vision based method with pedestrian detection as its application (a sub-part of the French national project “LOVe”: Logiciels d'Observation des Vulnerables). With a prospect of benefiting from an innovative method i.e. the genetic evolutionary “flies” method proposed by former researchers on continuous data updating and asynchronous data reading, we have carried on the “flies” method through the task of pedestrian detection affiliated with the “LOVe” project. Compared with former work of the “flies” method, two main contributions have been incorporated into the architecture of the “flies” method: first, an improved fitness function has been proposed instead of the original one; second, a technique coined “concentrating” has been integrated into the evolution procedure. The improved “flies” method is used to offer range information of possible objects in the detection field. The integrate scheme of pedestrian detection is presented as well. Some experimental results are given for validating the performance improvements brought by the improved “flies” method and for validating the pedestrian detection method based on the improved “flies” method

Stereo Vision-based Estimation of 3D Position and Axial Motion of Road Obstacles

International audienceIn this article, we present a fast and accurate stereo vision-based system that detects and tracks road obstacles, and computes their 3D position and their axial motion. To do so, axial motion maps are constructed and the inclination angles of 3D straight segments are computed. 3D straight segments are obtained after the construction of 3D sparse maps based on dynamic programming and multi-criteria analysis. Axial motion maps are computed from a sequence of dense 3D maps without region matching