Technical University of Munich

@inproceedings{hoyningen-huene_robust_2009,
 author = {Nvon Hoyningen-Huene and M Beetz},
 title = {Robust real-time multiple target tracking},
 booktitle = {Ninth Asian Conference on Computer Vision ({ACCV)}},
 year = {2009},
 address = {Xi'an, China},
 month = {sep},
 abstract = {We propose a novel efficient algorithm for robust tracking of a fixed
	number of targets in real-time with low failure rate. The method
	is an instance of Sequential Importance Resampling filters approximating
	the posterior of complete target configurations as a mixture of Gaussians.
	Using predicted target positions by Kalman filters, data associations
	are sampled for each measurement sweep according to their likelihood
	allowing to constrain the number of associations per target. Updated
	target configurations are weighted for resampling pursuant to their
	explanatory power for former positions and measurements. Fixed-lag
	of the resulting positions increases the tracking quality while smart
	resampling and memoization decrease the computational demand. A negative
	information handling exploits missing measurements for a target outside
	the monitored area. We present both, qualitative and quantitative
	experimental results on two demanding real-world applications with
	occluded and highly confusable targets, demonstrating the robustness
	and real-time performance of our approach outperforming current state-of-the-art
	{MCMC} methods.},
}