title: Identifying and tracking pedestrians based on sensor fusion and motion stability predictions.
abstract: The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem) and dense disparity maps and u-v disparity (vision subsystem). Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle.
authors.affiliation: Intelligent Systems Laboratory, Universidad Carlos III de Madrid/ Avda de la Universidad 30, 28911 Leganés, Madrid, Spain. bmusleh@ing.uc3m.es
authors.forename: Basam
authors.initials: B
authors.lastname: Musleh
authors.forename: Fernando
authors.initials: F
authors.lastname: García
authors.forename: Javier
authors.initials: J
authors.lastname: Otamendi
authors.forename: José Maria
authors.initials: JM
authors.lastname: Armingol
authors.forename: Arturo
authors.initials: A
authors.lastname: de la Escalera
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001334:Automobile Driving; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D016138:Walking