Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking and modeling people in video sequences
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Markerless motion capture of complex full-body movement for character animation
Proceedings of the Eurographic workshop on Computer animation and simulation
Tracking Multiple Humans in Complex Situations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Multiple Objects through Occlusions
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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In this paper we present a management model to deal with the problem of tracking a large number of features during long image sequences. Some usual difficulties are related to this problem: features may be temporarily occluded or might even have disappeared definitively; the computational cost involved should always be reduced to the strictly necessary. The proposed Net Present Value (NPV) model, based on the economic Theory of Capital, considers the tracking of each missing feature as an investment. Thus, using the NPV criterion, with adequate receipt and outlay functions, each occluded feature may be kept on tracking or it may be excluded of the tracking process depending on its historical behavior. This methodology may be applied to any tracking system as long as the tracking results may be evaluated in each temporal step. Experimental results, both on synthetic and real image sequences, which validate our model, will be also presented.