@conference {12560,
	title = {Optimal Multi-View Fusion of Object Locations},
	booktitle = {Motion and video Computing, 2008. WMVC 2008. IEEE Workshop on},
	year = {2008},
	month = {2008/01//},
	pages = {1 - 8},
	abstract = {In surveillance applications, it is common to have multiple cameras observing targets exhibiting motion on a ground plane. Tracking and estimation of the location of a target on the plane becomes an important inference problem. In this paper, we study the problem of combining estimates of location obtained from multiple cameras. We model the relation between the uncertainty in the location estimation to the position and location of the camera with respect to the plane (which is encoded by a 2D projective transformation). This is addressed by a theoretical study of the properties of a random variable under a projective transformation and analysis of the geometric setting when the moments of the transformed random variable exist. In this context, we prove that ground plane tracking near the horizon line is often inaccurate. Using suitable approximations to compute the moments, a minimum variance estimator is designed to fuse the multi-camera location estimates. Finally, we present experimental results that illustrate the importance of such modeling in location estimation and tracking.},
	keywords = {control;surveillance;tracking;, detection;position, estimates;optimal, estimation;location, estimation;multicamera, estimation;object, estimator;motion, fusion;surveillance;cameras;estimation, location, multiview, theory;motion, tracking;minimum, variance},
	doi = {10.1109/WMVC.2008.4544048},
	author = {Sankaranarayanan,A. C and Chellapa, Rama}
}
@conference {12608,
	title = {Shape-Regulated Particle Filtering for Tracking Non-Rigid Objects},
	booktitle = {Image Processing, 2006 IEEE International Conference on},
	year = {2006},
	month = {2006/10//},
	pages = {2813 - 2816},
	abstract = {This paper presents an active contour based algorithm for tracking non-rigid objects in heavily cluttered scenes. We decompose the non-rigid contour tracking problem into three subproblems: 2D motion estimation, deformation detection, and shape regulation. First, we employ a particle filter to estimate the affine transform parameters between successive frames. Second, by using a dynamic object model, we generate a probabilistic map of deformation to reshape its contour. Finally, we project the updated model onto a trained shape subspace to constrain deformations to be within possible object appearances. Our experiments show that the proposed algorithm significantly improves the performance of the tracker},
	keywords = {(numerical, 2D, algorithm;affine, based, contour, detection;dynamic, detection;motion, detection;parameter, estimation;active, estimation;object, estimation;particle, Filtering, filtering;probabilistic, filters;, map;shape-regulation;affine, methods);tracking, model;nonrigid, MOTION, object, scenes;deformation, tracking;parameter, transform;cluttered, transforms;clutter;edge},
	doi = {10.1109/ICIP.2006.312993},
	author = {Jie Shao and Chellapa, Rama and Porikli, F.}
}
