Authors:
Dora Luz Almanza-Ojeda
;
Michel Devy
and
Ariane Herbulot
Affiliation:
Université de Toulouse, France
Keyword(s):
Moving obstacles, Detection, Tracking, Clustering, Monocular vision.
Related
Ontology
Subjects/Areas/Topics:
Image Processing
;
Informatics in Control, Automation and Robotics
;
Robotics and Automation
;
Vision, Recognition and Reconstruction
Abstract:
This paper presents a methodology for detecting and tracking moving objects during mobile robot navigation in unknown environments using only visual information. An initial set of interest points is detected and then
tracked by the Kanade-Lucas tracker (KLT). Along few images, point positions and velocities are accumulated and a spatio-temporal analysis, based on the a contrario theory, is performed for the clustering process of these points. All dynamical sets of points found by the clustering are directly initialized and tracked as moving objects using Kalman Filter. At each image, a probability map saves temporally the previous interesting point positions with a certain probability value. New features will be added in the most likely zones based on this probability map. The process detection-clustering-tracking is executed in an iterative way to guarantee the detection of new moving objects or to incrementally enlarge already detected objects until their real limits. Experimental
results on real dynamic images acquired during robot outdoor and indoor navigation task are
presented. Furthermore, rigid and non rigid moving object tracking results are compared and discussed.
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