Authors:
Hassan M. Nemati
;
Saeed Gholami Shahbandi
and
Björn Åstrand
Affiliation:
Halmstad University, Sweden
Keyword(s):
Detection and Tracking Moving Objects, Extended Kalman Filter, Human Tracking, Occlusion, Intelligent Vehicles, Mobile Robots.
Related
Ontology
Subjects/Areas/Topics:
Autonomous Agents
;
Informatics in Control, Automation and Robotics
;
Mobile Robots and Autonomous Systems
;
Perception and Awareness
;
Robotics and Automation
Abstract:
Relying on the commonsense knowledge that the trajectory of any physical entity in the spatio-temporal domain
is continuous, we propose a heuristic data association technique. The technique is used in conjunction
with an Extended Kalman Filter (EKF) for human tracking under occlusion. Our method is capable of tracking
moving objects, maintain their state hypothesis even in the period of occlusion, and associate the target reappeared
from occlusion with the existing hypothesis. The technique relies on the estimation of the reappearance
event both in time and location, accompanied with an alert signal that would enable more intelligent behavior
(e.g. in path planning). We implemented the proposed method, and evaluated its performance with real-world
data. The result validates the expected capabilities, even in case of tracking multiple humans simultaneously.