Abstract
This paper describes a method proposed for the detection, the tracking and the identification of mobile objects, detected from a mobile camera, typically a camera embedded on a robot. A global architecture is presented, using only vision, in order to solve simultaneously several problems: the camera (or vehicle) Localization, the environment Mapping and the Detection and Tracking of Moving Objects. The goal is to build a convenient description of a dynamic scene from vision: what is static? What is dynamic? where is the robot? how do other mobile objects move? It is proposed to combine two approaches; first a Clustering method allows to detect static points, to be used by the SLAM algorithm and dynamic ones, to segment and estimate the status of mobile objects. Second a classification approach allows to identify objects of known classes in image regions. These two approaches are combined in an active method based in a Motion Grid in order to select actively where to look for mobile objects. The overall approach is evaluated with real data acquired indoor and outdoor from a camera embedded on a mobile robot.
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Márquez-Gámez, D., Devy, M. (2012). Active Visual-Based Detection and Tracking of Moving Objects from Clustering and Classification Methods. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P., Zemčík, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2012. Lecture Notes in Computer Science, vol 7517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33140-4_32
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DOI: https://doi.org/10.1007/978-3-642-33140-4_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33139-8
Online ISBN: 978-3-642-33140-4
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