@article {12540,
	title = {Synthesis of Silhouettes and Visual Hull Reconstruction for Articulated Humans},
	journal = {Multimedia, IEEE Transactions on},
	volume = {10},
	year = {2008},
	month = {2008/12//},
	pages = {1565 - 1577},
	abstract = {In this paper, we propose a complete framework for improved synthesis and understanding of the human pose from a limited number of silhouette images. It combines the active image-based visual hull (IBVH) algorithm and a contour-based body part segmentation technique. We derive a simple, approximate algorithm to decide the extrinsic parameters of a virtual camera, and synthesize the turntable image collection of the person using the IBVH algorithm by actively moving the virtual camera on a properly computed circular trajectory around the person. Using the turning function distance as the silhouette similarity measurement, this approach can be used to generate the desired pose-normalized images for recognition applications. In order to overcome the inability of the visual hull (VH) method to reconstruct concave regions, we propose a contour-based human body part localization algorithm to segment the silhouette images into convex body parts. The body parts observed from the virtual view are generated separately from the corresponding body parts observed from the input views and then assembled together for a more accurate VH reconstruction. Furthermore, the obtained turntable image collection helps to improve the body part segmentation and identification process. By using the inner distance shape context (IDSC) measurement, we are able to estimate the body part locations more accurately from a synthesized view where we can localize the body part more precisely. Experiments show that the proposed algorithm can greatly improve body part segmentation and hence shape reconstruction results.},
	keywords = {active, algorithm;articulated, algorithm;inner, body, camera;visual, collection;virtual, computation;contour-based, Context, detection;image, distance, distance;turntable, estimation;shape, function, hull, human, image, image;approximate, localization, measurement;silhouette, measurement;turning, part, pose;circular, reality;, recognition;virtual, reconstruction;approximation, reconstruction;image, segmentation, segmentation;pose, SHAPE, similarity, synthesis;silhouette, technique;human, theory;cameras;edge, Trajectory},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2008.2007321},
	author = {Yue,Zhanfeng and Chellapa, Rama}
}
@conference {19665,
	title = {Multitarget tracking with split and merged measurements},
	booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005},
	volume = {1},
	year = {2005},
	month = {2005/06//},
	pages = {605 - 610 vol. 1},
	abstract = {In many multitarget tracking applications in computer vision, a detection algorithm provides locations of potential targets. Subsequently, the measurements are associated with previously estimated target trajectories in a data association step. The output of the detector is often imperfect and the detection data may include multiple, split measurements from a single target or a single merged measurement from several targets. To address this problem, we introduce a multiple hypothesis tracker for interacting targets that generate split and merged measurements. The tracker is based on an efficient Markov chain Monte Carlo (MCMC) based auxiliary variable particle filter. The particle filter is Rao-Blackwellized such that the continuous target state parameters are estimated analytically, and an MCMC sampler generates samples from the large discrete space of data associations. In addition, we include experimental results in a scenario where we track several interacting targets that generate these split and merged measurements.},
	keywords = {Application software, Computer vision, Detection algorithms, Detectors, filtering theory, Markov chain Monte Carlo based auxiliary variable particle filter, Markov processes, merged measurements, Monte Carlo methods, multiple hypothesis tracker, multitarget tracking, parameter estimation, Particle filters, Particle tracking, Rao-Blackwellized filter, split measurements, target tracking, Trajectory},
	author = {Zia Khan and Balch, T. and Dellaert, F.}
}
@conference {19670,
	title = {What Are the Ants Doing? Vision-Based Tracking and Reconstruction of Control Programs},
	booktitle = {Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005. ICRA 2005},
	year = {2005},
	month = {2005/04//},
	pages = {4182 - 4187},
	abstract = {In this paper, we study the problem of going from a real-world, multi-agent system to the generation of control programs in an automatic fashion. In particular, a computer vision system is presented, capable of simultaneously tracking multiple agents, such as social insects. Moreover, the data obtained from this system is fed into a mode-reconstruction module that generates low-complexity control programs, i.e. strings of symbolic descriptions of control-interrupt pairs, consistent with the empirical data. The result is a mechanism for going from the real system to an executable implementation that can be used for controlling multiple mobile robots.},
	keywords = {Animals, Automatic generation control, Biological information theory, Computer vision, Control systems, Mobile robots, Probability distribution, Robot control, target tracking, Trajectory},
	author = {Egerstedt, M. and Balch, T. and Dellaert, F. and Delmotte, F. and Zia Khan}
}
@conference {19659,
	title = {Efficient particle filter-based tracking of multiple interacting targets using an MRF-based motion model},
	booktitle = {2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings},
	volume = {1},
	year = {2003},
	month = {2003/10//},
	pages = {254 - 259 vol.1},
	abstract = {We describe a multiple hypothesis particle filter for tracking targets that are influenced by the proximity and/or behavior of other targets. Our contribution is to show how a Markov random field motion prior, built on the fly at each time step, can model these interactions to enable more accurate tracking. We present results for a social insect tracking application, where we model the domain knowledge that two targets cannot occupy the same space, and targets actively avoid collisions. We show that using this model improves track quality and efficiency. Unfortunately, the joint particle tracker we propose suffers from exponential complexity in the number of tracked targets. An approximation to the joint filter, however, consisting of multiple nearly independent particle filters can provide similar track quality at substantially lower computational cost.},
	keywords = {collision avoidance, computational cost, Computational efficiency, Educational institutions, exponential complexity, Filtering, filtering theory, Insects, joint particle tracker, Markov processes, Markov random field motion, Markov random fields, multiple interacting targets, particle filter-based tracking, Particle filters, Particle tracking, Radar tracking, social insect tracking application, target tracking, Trajectory},
	author = {Zia Khan and Balch, T. and Dellaert, F.}
}
