This article presents a solution to track people across a network of cameras with disjoint fields of vision. First, an appearance-based signature is extracted from each frame of the sequence characterizing the passage of a person. This feature, called color-position signature, merges spatial and color information for improved robustness. Moreover, an illuminant invariant procedure has been introduced to manage lighting and camera response changes. Second, the distance between 2 sequences is estimated with dimensionality reduction techniques. Two methods are implemented and compared. The first one is the classical, linear, principal components analysis, whereas the second is a recent nonlinear method called the Laplacian eigenmaps approach. Results from 2 databases acquired in difficult conditions show that the Laplacian eigenmaps method always yields the best results, which confirms the interest of nonlinear approaches in such applications. Moreover, these results show the relevance of this approach combining appearance-based signature and dimensionality reduction technique within the scope of people tracking across distinct fields of vision.


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    Title :

    Intelligent Distributed Surveillance System for People Reidentification in a Transportation Environment



    Published in:

    Publication date :

    2011-07-01


    Size :

    14 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English






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