A Study of Feature Combination for Vehicle Detection Based on Image Processing


== Abstract ==

Video analytics play a critical role in most recent traffic monitoring and driver assistance systems. In this context, the correct detection and classification of surrounding vehicles through image analysis has been the focus of extensive research in the last years. Most of the pieces of work reported for image-based vehicle verification make use of supervised classification approaches and resort to techniques, such as histograms of oriented gradients (HOG), principal component analysis (PCA), and Gabor filters, among others. Unfortunately, existing approaches are lacking in two respects: first, comparison between methods using a common body of work has not been addressed; second, no study of the combination potentiality of popular features for vehicle classification has been reported. In this study the performance of the different techniques is first reviewed and compared using a common public database. Then, the combination capabilities of these techniques are explored and a methodology is presented for the fusion of classifiers built upon them, taking into account also the vehicle pose. The study unveils the limitations of single-feature based classification and makes clear that fusion of classifiers is highly beneficial for vehicle verification. This work was partially supported by the Ministerio de Economía y Competitividad of the Spanish Government under Project TEC2010-20412 (Enhanced 3D TV).

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_185724140-beopen1203-8396-document.pdf</pdf>


== Original document ==

The different versions of the original document can be found in:

* [http://downloads.hindawi.com/journals/tswj/2014/196251.pdf http://downloads.hindawi.com/journals/tswj/2014/196251.pdf] under the license https://creativecommons.org/licenses/by

* [http://dx.doi.org/10.1155/2014/196251 http://dx.doi.org/10.1155/2014/196251] under the license cc-by

* [http://europepmc.org/articles/PMC3932223 http://europepmc.org/articles/PMC3932223] under the license http://creativecommons.org/licenses/by/3.0

* [http://hdl.handle.net/10486/666442 http://hdl.handle.net/10486/666442]

* [http://oa.upm.es/37441 http://oa.upm.es/37441]

* [https://dx.doi.org/10.1155/2014/196251 https://dx.doi.org/10.1155/2014/196251] under the license http://creativecommons.org/licenses/by-nc-nd/3.0/es/

* [http://downloads.hindawi.com/journals/tswj/2014/196251.pdf http://downloads.hindawi.com/journals/tswj/2014/196251.pdf],
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* [http://dx.doi.org/10.1155/2014/196251 http://dx.doi.org/10.1155/2014/196251],
: [https://doaj.org/toc/2356-6140 https://doaj.org/toc/2356-6140],
: [https://doaj.org/toc/1537-744X https://doaj.org/toc/1537-744X] under the license http://creativecommons.org/licenses/by/3.0/

* [https://www.hindawi.com/journals/tswj/2014/196251 https://www.hindawi.com/journals/tswj/2014/196251],
: [http://downloads.hindawi.com/journals/tswj/2014/196251.pdf http://downloads.hindawi.com/journals/tswj/2014/196251.pdf],
: [http://oa.upm.es/37441 http://oa.upm.es/37441],
: [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3932223 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3932223],
: [http://europepmc.org/articles/PMC3932223 http://europepmc.org/articles/PMC3932223],
: [http://oa.upm.es/37441/1/INVE_MEM_2014_196739.pdf http://oa.upm.es/37441/1/INVE_MEM_2014_196739.pdf],
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: [https://repositorio.uam.es/handle/10486/666442 https://repositorio.uam.es/handle/10486/666442],
: [https://academic.microsoft.com/#/detail/1970845820 https://academic.microsoft.com/#/detail/1970845820]