@conference {13108,
	title = {Assigning cameras to subjects in video surveillance systems},
	booktitle = {Robotics and Automation, 2009. ICRA {\textquoteright}09. IEEE International Conference on},
	year = {2009},
	month = {2009/05//},
	pages = {837 - 843},
	abstract = {We consider the problem of tracking multiple agents moving amongst obstacles, using multiple cameras. Given an environment with obstacles, and many people moving through it, we construct a separate narrow field of view video for as many people as possible, by stitching together video segments from multiple cameras over time. We employ a novel approach to assign cameras to people as a function of time, with camera switches when needed. The problem is modeled as a bipartite graph and the solution corresponds to a maximum matching. As people move, the solution is efficiently updated by computing an augmenting path rather than by solving for a new matching. This reduces computation time by an order of magnitude. In addition, solving for the shortest augmenting path minimizes the number of camera switches at each update. When not all people can be covered by the available cameras, we cluster as many people as possible into small groups, then assign cameras to groups using a minimum cost matching algorithm. We test our method using numerous runs from different simulators.},
	keywords = {agent, algorithm;multiple, assignment;computation, augmenting, cameras;video, cost, detection;video, graph;camera, MATCHING, matching;minimum, matching;target, path;bipartite, reduction;maximum, segment;video, Surveillance, surveillance;, system;graph, theory;image, TIME, tracking;obstacle, tracking;video, video},
	doi = {10.1109/ROBOT.2009.5152753},
	author = {El-Alfy,H. and Jacobs, David W. and Davis, Larry S.}
}
@article {12545,
	title = {Object Detection, Tracking and Recognition for Multiple Smart Cameras},
	journal = {Proceedings of the IEEE},
	volume = {96},
	year = {2008},
	month = {2008/10//},
	pages = {1606 - 1624},
	abstract = {Video cameras are among the most commonly used sensors in a large number of applications, ranging from surveillance to smart rooms for videoconferencing. There is a need to develop algorithms for tasks such as detection, tracking, and recognition of objects, specifically using distributed networks of cameras. The projective nature of imaging sensors provides ample challenges for data association across cameras. We first discuss the nature of these challenges in the context of visual sensor networks. Then, we show how real-world constraints can be favorably exploited in order to tackle these challenges. Examples of real-world constraints are (a) the presence of a world plane, (b) the presence of a three-dimiensional scene model, (c) consistency of motion across cameras, and (d) color and texture properties. In this regard, the main focus of this paper is towards highlighting the efficient use of the geometric constraints induced by the imaging devices to derive distributed algorithms for target detection, tracking, and recognition. Our discussions are supported by several examples drawn from real applications. Lastly, we also describe several potential research problems that remain to be addressed.},
	keywords = {algorithm;geometric, analysis;image, association;distributed, camera;visual, cameras;, cameras;object, colour, constraints;imaging, data, detection;object, detection;sensor, detection;three-dimiensional, fusion;target, model;video, network;distributed, recognition;object, scene, sensor, sensor;multiple, sensors;geometry;image, sensors;object, smart, texture;intelligent, tracking;target, tracking;video},
	isbn = {0018-9219},
	doi = {10.1109/JPROC.2008.928758},
	author = {Sankaranarayanan,A. C and Veeraraghavan,A. and Chellapa, Rama}
}
@article {12587,
	title = {Target Tracking Using a Joint Acoustic Video System},
	journal = {Multimedia, IEEE Transactions on},
	volume = {9},
	year = {2007},
	month = {2007/06//},
	pages = {715 - 727},
	abstract = {In this paper, a multitarget tracking system for collocated video and acoustic sensors is presented. We formulate the tracking problem using a particle filter based on a state-space approach. We first discuss the acoustic state-space formulation whose observations use a sliding window of direction-of-arrival estimates. We then present the video state space that tracks a target{\textquoteright}s position on the image plane based on online adaptive appearance models. For the joint operation of the filter, we combine the state vectors of the individual modalities and also introduce a time-delay variable to handle the acoustic-video data synchronization issue, caused by acoustic propagation delays. A novel particle filter proposal strategy for joint state-space tracking is introduced, which places the random support of the joint filter where the final posterior is likely to lie. By using the Kullback-Leibler divergence measure, it is shown that the joint operation of the filter decreases the worst case divergence of the individual modalities. The resulting joint tracking filter is quite robust against video and acoustic occlusions due to our proposal strategy. Computer simulations are presented with synthetic and field data to demonstrate the filter{\textquoteright}s performance},
	keywords = {(numerical, acoustic, adaptive, appearance, approach;synchronization;time-delay, data, delay;acoustic, divergence;acoustic, estimate;joint, estimation;hidden, feature, filter;sliding, Filtering, fusion;multitarget, fusion;synchronisation;target, highways;direction-of-arrival, Kullback-Leibler, methods);sensor, model;particle, processing;, processing;automated, propagation, removal;optical, signal, system;multimodal, tracking;acoustic, tracking;direction-of-arrival, tracking;occlusion;online, tracking;particle, tracking;video, variable;visual, video, window;state-space},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2007.893340},
	author = {Cevher, V. and Sankaranarayanan,A. C and McClellan, J.H. and Chellapa, Rama}
}
