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Distributed Compression and Fusion of Nonnegative Sparse Signals for Multiple-View Object Recognition

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Executive Summary

Visual surveillance in complex urban environments requires an intelligent system to automatically track and identify multiple objects of interest in a network of distributed cameras. The ability to perform robust object recognition is critical to compensate adverse conditions and improve performance, such as multi-object association, visual occlusion, and data fusion with hybrid sensor modalities. In this paper, the authors propose an efficient distributed data compression and fusion scheme to encode and transmit SIFT-based visual histograms in a multi-hop network to perform accurate 3-D object recognition. The method harnesses an emerging theory of (distributed) compressive sensing to encode high-dimensional, nonnegative sparse signals via random projection, which is unsupervised and independent to the sensor modality.

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