@article {19662,
	title = {MCMC Data Association and Sparse Factorization Updating for Real Time Multitarget Tracking with Merged and Multiple Measurements},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {28},
	year = {2006},
	month = {2006/12//},
	pages = {1960 - 1972},
	abstract = {In several multitarget tracking applications, a target may return more than one measurement per target and interacting targets may return multiple merged measurements between targets. Existing algorithms for tracking and data association, initially applied to radar tracking, do not adequately address these types of measurements. Here, we introduce a probabilistic model for interacting targets that addresses both types of measurements simultaneously. We provide an algorithm for approximate inference in this model using a Markov chain Monte Carlo (MCMC)-based auxiliary variable particle filter. We Rao-Blackwellize the Markov chain to eliminate sampling over the continuous state space of the targets. A major contribution of this work is the use of sparse least squares updating and downdating techniques, which significantly reduce the computational cost per iteration of the Markov chain. Also, when combined with a simple heuristic, they enable the algorithm to correctly focus computation on interacting targets. We include experimental results on a challenging simulation sequence. We test the accuracy of the algorithm using two sensor modalities, video, and laser range data. We also show the algorithm exhibits real time performance on a conventional PC},
	keywords = {algorithms, approximate inference, Artificial intelligence, auxiliary variable particle filter, Computational efficiency, continuous state space, downdating, Image Enhancement, Image Interpretation, Computer-Assisted, Inference algorithms, Information Storage and Retrieval, laser range scanner, laser range scanner., Least squares approximation, least squares approximations, Least squares methods, linear least squares, Markov chain Monte Carlo, Markov processes, MCMC data association, merged measurements, Monte Carlo methods, Movement, multiple merged measurements, multitarget tracking, particle filter, particle filtering (numerical methods), Particle filters, Pattern Recognition, Automated, probabilistic model, QR factorization, Radar tracking, Rao-Blackwellized, real time multitarget tracking, Reproducibility of results, Sampling methods, Sensitivity and Specificity, sensor fusion, sparse factorization updating, sparse least squares, State-space methods, Subtraction Technique, target tracking, updating},
	isbn = {0162-8828},
	author = {Zia Khan and Balch, T. and Dellaert, F.}
}
@conference {11965,
	title = {Statistical biases in optic flow},
	booktitle = {Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.},
	volume = {1},
	year = {1999},
	month = {1999///},
	pages = {566 Vol. 1 - 566 Vol. 1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The computation of optical flow from image derivatives is biased in regions of non uniform gradient distributions. A least-squares or total least squares approach to computing optic flow from image derivatives even in regions of consistent flow can lead to a systematic bias dependent upon the direction of the optic flow, the distribution of the gradient directions, and the distribution of the image noise. The bias a consistent underestimation of length and a directional error. Similar results hold for various methods of computing optical flow in the spatiotemporal frequency domain. The predicted bias in the optical flow is consistent with psychophysical evidence of human judgment of the velocity of moving plaids, and provides an explanation of the Ouchi illusion. Correction of the bias requires accurate estimates of the noise distribution; the failure of the human visual system to make these corrections illustrates both the difficulty of the task and the feasibility of using this distorted optic flow or undistorted normal flow in tasks requiring higher lever processing},
	keywords = {Distributed computing, Frequency domain analysis, HUMANS, image derivatives, Image motion analysis, Image sequences, Least squares methods, Motion estimation, Optical computing, Optical distortion, optical flow, Optical noise, Ouchi illusion, perception of motion, Psychology, Spatiotemporal phenomena, statistical analysis, systematic bias, total least squares},
	isbn = {0-7695-0149-4},
	doi = {10.1109/CVPR.1999.786994},
	author = {Ferm{\"u}ller, Cornelia and Pless, R. and Aloimonos, J.}
}
@conference {11957,
	title = {Optimal Computing Of Structure From Motion Using Point Correspondences In Two Frames},
	booktitle = {Proceedings of Second International Conference on Computer Vision},
	year = {1988},
	month = {1988/12/05/8},
	pages = {449 - 453},
	publisher = {IEEE},
	organization = {IEEE},
	keywords = {Automation, Computer vision, Educational institutions, Gaussian noise, Image motion analysis, Laboratories, Least squares approximation, Least squares methods, Motion estimation, Optical computing},
	isbn = {0-8186-0883-8},
	doi = {10.1109/CCV.1988.590022},
	author = {Spetsakis, M. E and Aloimonos, J.}
}
