Vehicle Color Extraction Based on First Sight Window

Xu, Zhan; Cao, Jin

doi:10.1109/icise.2009.1334

Cited by 4 publications

(1 citation statement)

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“…For example, although color is extremely useful and one of the first attributes to be used by people to describe vehicles, the ability to accurately characterize vehicle color has been extremely challenging. Although, there are many publications describing research in this area [1][2][3][4][5][6][7][8], commercial systems provide only a rudimentary ability to search for vehicles based on color and there are no industrial publications describing their accuracy.…”

Section: Introductionmentioning

confidence: 99%

Tree-based vehicle color classification using spatial features on publicly available continuous data

Brown

Datta

Pankanti

2013

2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance

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Several recent investigations attempt to classify vehicles into a small number (5-7) of colors. A significant complication arises, however; a large proportion of vehicles (>50%) are various shades of gray: white, black, silver, gray, and variations such as gun metal and pearly white. Distinguishing such shades of gray in vehicle body color from lighting changes is an unsolved problem. Furthermore, previous studies have evaluated their performance on private datasets precluding a comparison of methodologies. In this paper, we release a public dataset with ground truth color classification for future evaluations and comparisons based on the publicly available i-LIDS data [9]. We describe a method to perform vehicle color classification into 7 frequently occurring colors including dark red, dark blue and light silver, using pose dependent vehicle detection, vehicle alignment, and vehicle body part masks. We introduce new features for tree-based vehicle color classification based on the reliability of color information and the relative color of various vehicle parts.978-1-4799-0703-8/13/$31.00 ©2013 IEEE

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