Independent Motion: The Importance of History
Abstract
We consider a problem central in aerial visual surveillance applications-detection and tracking of small, independently moving objects in long and noisy video sequences. We directly use spatiotemporal image intensity gradient measurements to compute an exact model of background motion. This allows the creation of accurate mosaics over many frames and the definition of a constraint violation function which acts as an indication of independent motion. A novel temporal integration method maintains confidence measures over long subsequences without computing the optic flow, requiring object models, or using a Kalman filler. The mosaic acts as a stable feature frame, allowing precise localization of the independently moving objects. We present a statistical analysis of the effects of image noise on the constraint violation measure and find a good match between the predicted probability distribution function and the measured sample frequencies in a test sequence.
Cite
Text
Pless et al. "Independent Motion: The Importance of History." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999. doi:10.1109/CVPR.1999.784614Markdown
[Pless et al. "Independent Motion: The Importance of History." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999.](https://mlanthology.org/cvpr/1999/pless1999cvpr-independent/) doi:10.1109/CVPR.1999.784614BibTeX
@inproceedings{pless1999cvpr-independent,
title = {{Independent Motion: The Importance of History}},
author = {Pless, Robert and Brodský, Tomás and Aloimonos, Yiannis},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {1999},
pages = {2092-2097},
doi = {10.1109/CVPR.1999.784614},
url = {https://mlanthology.org/cvpr/1999/pless1999cvpr-independent/}
}