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<p><b>Abstract</b>—This paper presents a comprehensive framework for tracking coarse human models from sequences of synchronized monocular grayscale images in multiple camera coordinates. It demonstrates the feasibility of an end-to-end person tracking system using a unique combination of motion analysis on 3D geometry in different camera coordinates and other existing techniques in motion detection, segmentation, and pattern recognition. The system starts with tracking from a single camera view. When the system predicts that the active camera will no longer have a good view of the subject of interest, tracking will be switched to another camera which provides a better view and requires the least switching to continue tracking. The nonrigidity of the human body is addressed by matching points of the middle line of the human image, spatially and temporally, using Bayesian classification schemes. Multivariate normal distributions are employed to model class-conditional densities of the features for tracking, such as location, intensity, and geometric features. Limited degrees of occlusion are tolerated within the system. Experimental results using a prototype system are presented and the performance of the algorithm is evaluated to demonstrate its feasibility for real time applications.</p>
Tracking, human modeling, motion estimation, multiple perspectives, Bayesian classification, end-to-end vision systems.

J. Aggarwal and Q. Cai, "Tracking Human Motion in Structured Environments Using a Distributed-Camera System," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 21, no. , pp. 1241-1247, 1999.
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