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Proceedings Paper

Object tracking with adaptive HOG detector and adaptive Rao-Blackwellised particle filter
Author(s): Stefano Rosa; Marco Paleari; Paolo Ariano; Basilio Bona
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Paper Abstract

Scenarios for a manned mission to the Moon or Mars call for astronaut teams to be accompanied by semiautonomous robots. A prerequisite for human-robot interaction is the capability of successfully tracking humans and objects in the environment. In this paper we present a system for real-time visual object tracking in 2D images for mobile robotic systems. The proposed algorithm is able to specialize to individual objects and to adapt to substantial changes in illumination and object appearance during tracking. The algorithm is composed by two main blocks: a detector based on Histogram of Oriented Gradient (HOG) descriptors and linear Support Vector Machines (SVM), and a tracker which is implemented by an adaptive Rao-Blackwellised particle filter (RBPF). The SVM is re-trained online on new samples taken from previous predicted positions. We use the effective sample size to decide when the classifier needs to be re-trained. Position hypotheses for the tracked object are the result of a clustering procedure applied on the set of particles. The algorithm has been tested on challenging video sequences presenting strong changes in object appearance, illumination, and occlusion. Experimental tests show that the presented method is able to achieve near real-time performances with a precision of about 7 pixels on standard video sequences of dimensions 320 × 240.

Paper Details

Date Published: 23 January 2012
PDF: 8 pages
Proc. SPIE 8301, Intelligent Robots and Computer Vision XXIX: Algorithms and Techniques, 83010W (23 January 2012); doi: 10.1117/12.911991
Show Author Affiliations
Stefano Rosa, Italian Institute of Technology (Italy)
Marco Paleari, Italian Institute of Technology (Italy)
Paolo Ariano, Italian Institute of Technology (Italy)
Basilio Bona, Politecnico di Torino (Italy)


Published in SPIE Proceedings Vol. 8301:
Intelligent Robots and Computer Vision XXIX: Algorithms and Techniques
Juha Röning; David P. Casasent, Editor(s)

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