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Probabilistic Parameter-Free Motion Detection (2004)
| Venue: | IEEE Conf. Computer Vision and Pattern Recognition, CVPR’04, Washington DC |
| Citations: | 14 - 3 self |
BibTeX
@INPROCEEDINGS{Veit04probabilisticparameter-free,
author = {Thomas Veit and Frederic Cao and Patrick Bouthemy},
title = {Probabilistic Parameter-Free Motion Detection},
booktitle = {IEEE Conf. Computer Vision and Pattern Recognition, CVPR’04, Washington DC},
year = {2004},
pages = {715--721}
}
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Abstract
We propose an original probabilistic parameter-free method for the detection of independently moving objects in an image sequence. We apply a probabilistic perceptual principle, the Helmholtz principle, whose main advantage is the automatization of the detection decision, by providing a tight control of the number of false alarms. Not only does this method localize the moving objects but it also answers the preliminary question of the presence of motion. In particular, the method works even when no assumption on motion presence is made. The algorithm is composed of three independent steps: estimation of the dominant image motion, spatial segmentation of object boundaries and independent motion detection itself. We emphasize that none of these steps needs any parameter tuning. Results on real image sequences are reported and validate the proposed approach.
Keyphrases
probabilistic parameter-free motion detection main advantage preliminary question real image sequence motion presence original probabilistic parameter-free method helmholtz principle spatial segmentation detection decision probabilistic perceptual principle object boundary parameter tuning independent motion detection dominant image motion false alarm independent step image sequence tight control
