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13 June 2014 Sparse representation for vehicle recognition
Nathan D. Monnig, Wesam Sakla
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Proceedings Volume 9090, Automatic Target Recognition XXIV; 90900L (2014) https://doi.org/10.1117/12.2053462
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States
Abstract
The Sparse Representation for Classification (SRC) algorithm has been demonstrated to be a state-of-the-art algorithm for facial recognition applications. Wright et al. demonstrate that under certain conditions, the SRC algorithm classification performance is agnostic to choice of linear feature space and highly resilient to image corruption. In this work, we examined the SRC algorithm performance on the vehicle recognition application, using images from the semi-synthetic vehicle database generated by the Air Force Research Laboratory. To represent modern operating conditions, vehicle images were corrupted with noise, blurring, and occlusion, with representation of varying pose and lighting conditions. Experiments suggest that linear feature space selection is important, particularly in the cases involving corrupted images. Overall, the SRC algorithm consistently outperforms a standard k nearest neighbor classifier on the vehicle recognition task.
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Nathan D. Monnig and Wesam Sakla "Sparse representation for vehicle recognition", Proc. SPIE 9090, Automatic Target Recognition XXIV, 90900L (13 June 2014); https://doi.org/10.1117/12.2053462
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KEYWORDS
Image classification
Detection and tracking algorithms
Light sources and illumination
Principal component analysis
Reconstruction algorithms
Associative arrays
Evolutionary algorithms
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