title: Distributed pedestrian detection alerts based on data fusion with accurate localization.
abstract: Among Advanced Driver Assistance Systems (ADAS) pedestrian detection is a common issue due to the vulnerability of pedestrians in the event of accidents. In the present work, a novel approach for pedestrian detection based on data fusion is presented. Data fusion helps to overcome the limitations inherent to each detection system (computer vision and laser scanner) and provides accurate and trustable tracking of any pedestrian movement. The application is complemented by an efficient communication protocol, able to alert vehicles in the surroundings by a fast and reliable communication. The combination of a powerful location, based on a GPS with inertial measurement, and accurate obstacle localization based on data fusion has allowed locating the detected pedestrians with high accuracy. Tests proved the viability of the detection system and the efficiency of the communication, even at long distances. By the use of the alert communication, dangerous situations such as occlusions or misdetections can be avoided.
authors.affiliation: Intelligent Systems Laboratory, Universidad Carlos III de Madrid, Madrid, Spain. fegarcia@ing.uc3m.es
authors.forename: Fernando
authors.initials: F
authors.lastname: García
authors.forename: Felipe
authors.initials: F
authors.lastname: Jiménez
authors.forename: José Javier
authors.initials: JJ
authors.lastname: Anaya
authors.forename: José María
authors.initials: JM
authors.lastname: Armingol
authors.forename: José Eugenio
authors.initials: JE
authors.lastname: Naranjo
authors.forename: Arturo
authors.initials: A
authors.lastname: de la Escalera
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control*; D000465:Algorithms; D001185:Artificial Intelligence; D001334:Automobile Driving; D040362:Geographic Information Systems; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D051598:Whole Body Imaging / Q000379:methods*