An Improved Management Model for Tracking Missing Features in Computer Vision Long Image Sequences

• Type: Article in International Scientific Journal
• Date: 2006

Authors

Raquel R. Pinho (Author) Other; João Manuel R. S. Tavares (Author) FEUP; Miguel V. Correia (Author) FEUP

Journal

Title: WSEAS Transactions on Information Science and Applications

Vol. 3 No. 11

Pages: 2165-2170

ISSN: 1790-0832

Publisher: World Scientific and Engineering Academy and Society (WSEAS) Press

Indexing

Scopus

INSPEC

COMPENDEX

Scientific classification

CORDIS: Technological sciences > Engineering

Other information

Abstract (EN): In this paper we present a management model to deal with the problem of tracking missing features during long image sequences using Computational Vision. Some usual difficulties related with missing features are that they may be temporarily occluded or might even have disappeared definitively, and 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 approach may be applied to any tracking system as long as the tracking results may be evaluated in each temporal step, and it can deal with the appearance, occlusion and disappearance of features especially useful for tracking in long image sequences. Experimental results, both on synthetic and real image sequences, which validate our model, will be also presented.

Language: English

Type (Professor's evaluation): Scientific