Vehicle color recognition using monocular camera

Yang, Mei; Han, Guang; Li, Xiaofei; Zhu, Xiaoyan; Li, Liang

doi:10.1109/wcsp.2011.6096902

Cited by 20 publications

(12 citation statements)

References 2 publications

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“…An SVM-based classifier in a two-layer structure is then applied to classify the pixels in ROI. Yang et al [5] find the ROIs by using the geometric rule-based scheme. In this study, a coarse-to-fine strategy was adopted for efficiently identifying the ROIs from various types of vehicles.…”

Section: Location Of Region Of Interest (Roi)mentioning

confidence: 99%

“…In addition, the performance of this system was 18 frames per second. In order to show the effectiveness of the proposed method, several state-of-the-art algorithms [3][4][5]22,23] are implemented for the comparison. Color histogram-based features are widely used in color classification.…”

Section: Color Classificationmentioning

confidence: 99%

“…The combined histogram of length 328 is classified by a neural-network classifier. Yang et al [5] designed a twolayer classifier: HS color histogram for color classification in layer one and normalized RGB features for the blockgray-white classification in layer two. A two-stage classifier is proposed for color classification in [11,23].…”

Section: Color Classificationmentioning

confidence: 99%

“…Kim et al [4] quantized color features on channels H, S, and intensity (I) and found the best combination of features. In [5], the color histograms on channels H and S are classified to determine the red, yellow, green, and blue colors, while the normalized features on channels RGB are classified to determine the black, gray, and white colors. The choice of a color space is a critical issue in identifying color objects.…”

Section: Introductionmentioning

confidence: 99%

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Vehicle color classification using manifold learning methods from urban surveillance videos

et al. 2014

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Color identification of vehicles plays a significant role in crime detection. In this study, a novel scheme for the color identification of vehicles is proposed using the locating algorithm of regions of interest (ROIs) as well as the color histogram features from still images. A coarse-to-fine strategy was adopted to efficiently locate the ROIs for various vehicle types. Red patch labeling, geometrical-rule filtering, and a texture-based classifier were cascaded to locate the valid ROIs. A color space fusion together with a dimension reduction scheme was designed for color classification. Color histograms in ROIs were extracted and classified by a trained classifier. Seven different classes of color were identified in this work. Experiments were conducted to show the performance of the proposed method. The average rates of ROI location and color classification were 98.45% and 88.18%, respectively. Moreover, the classification efficiency of the proposed method was up to 18 frames per second.

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Section: Location Of Region Of Interest (Roi)mentioning

confidence: 99%

Section: Color Classificationmentioning

confidence: 99%

Section: Color Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vehicle color classification using manifold learning methods from urban surveillance videos

et al. 2014

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“…For vehicle color identification, the color histogram [7] is widely used. Yang et al [8] use the color histogram to form an array and identify the color with the hue that corresponds to certain location in the array.…”

Section: Introductionmentioning

confidence: 99%

A real-time architecture of multiple features extraction for vehicle verification

Wang

Cheng

2014

2014 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)

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In this paper, we propose a real-time architecture of multiple features extraction for vehicle verification. First, we set a range of YCbCr values to extract the pixels belonging to vehicle back lights. The density of light is computed by the number of the extracted pixels, and considered as the first feature. The second feature is the location of license plate. It is determined by Searching Area Decision, Local Edge Quantity, and Refinement. Then, the pixels belonging to back light and license plate are removed, and the averages of YCbCr values belonging to the remaining pixels are computed. It is considered as the third feature. Once the three features are computed, the relative error distance is applied to verify the vehicles in different frames. To achieve the request of real-time processing at 30fps, a four-staged pipeline architecture is also proposed. After synthesis, the total gate count is 390k and the operating frequency is 56MHz with TSMC 0.18 m process.

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Vehicle Discrimination Using a Combined Multiple Features Based on Vehicle Face

Wang

Kirui

et al. 2013

Lecture Notes in Electrical Engineering

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Vehicle color recognition using monocular camera

Cited by 20 publications

References 2 publications

Vehicle color classification using manifold learning methods from urban surveillance videos

Vehicle color classification using manifold learning methods from urban surveillance videos

A real-time architecture of multiple features extraction for vehicle verification

Vehicle Discrimination Using a Combined Multiple Features Based on Vehicle Face

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