Unsupervised and simultaneous training of multiple object detectors from unlabeled surveillance video

Authors:
Hasan Celik;Alan Hanjalic;Emile A. Hendriks
Affiliations:
Delft University of Technology, Information and Communication Theory Group, Mekelweg 4, 2628 CD Delft, The Netherlands;Delft University of Technology, Information and Communication Theory Group, Mekelweg 4, 2628 CD Delft, The Netherlands;Delft University of Technology, Information and Communication Theory Group, Mekelweg 4, 2628 CD Delft, The Netherlands
Venue:
Computer Vision and Image Understanding
Year:
2009

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Abstract

Object detection is an essential component in automated vision-based surveillance systems. In general, object detectors are constructed using training examples obtained from large annotated data sets. The inevitable limitations of typical training data sets make such supervised methods unsuitable for building generic surveillance systems applicable to a wide variety of scenes and camera setups. In our previous work we proposed an unsupervised method for learning and detecting the dominant object class in a general dynamic scene observed by a static camera. In this paper, we investigate the possibilities to expand the applicability of this method to the problem of multiple dominant object classes. We propose an idea on how to approach this expansion, and perform an evaluation of this idea using two representative surveillance video sequences.