Robust Real-Time Face Detection
International Journal of Computer Vision
Simultaneous localization, mapping and moving object tracking
Simultaneous localization, mapping and moving object tracking
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Avoiding moving outliers in visual SLAM by tracking moving objects
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Fusing Monocular Information in Multicamera SLAM
IEEE Transactions on Robotics
Inverse Depth Parametrization for Monocular SLAM
IEEE Transactions on Robotics
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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.