Vehicle classification technique for automated road traffic census
This thesis proposes the development of vehicle classification technique for automated road traffic census prototype to replace manually vehicle classification by applying morphological techniques for image classification. The developed prototype consists of three main phases: the first phase is v...
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| Main Author: | |
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| Format: | Final Year Project Report / IMRAD |
| Language: | en en |
| Published: |
Universiti Malaysia Sarawak, (UNIMAS)
2014
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/39058/1/Bong%20Shuk%20Hui%2824pgs%29.pdf http://ir.unimas.my/id/eprint/39058/4/Bong%20Shuk%20Hui%28ft%29.pdf http://ir.unimas.my/id/eprint/39058/ |
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| Summary: | This thesis proposes the development of vehicle classification technique for automated road traffic census
prototype to replace manually vehicle classification by applying morphological techniques for image
classification. The developed prototype consists of three main phases: the first phase is video frame preprocessing by applying thresholding, masking, image differencing, and median filter to obtain a resultant
image for vehicle detection algorithm. The second phase is vehicle detection algorithm by processing the
resultant image with 'Sobel ยท edge detection, first level dilation, binary filling of holes, boundary vehicle
elimination, second level binary dilation, and morphological binary open to obtain a final output image
for vehicle classification. Vehicle classification is taken into place by drawing a bounding box on the blob
in binary image. Blob analysis will be performed to calculate the area of pixels of the vehicle 's blob. The
vehicle will be classified based on the experimental area to classify vehicle into three categories which
are mini car, saloon car, and bus. The performance of the prototype is tested on three pre-recorded video
under homogenous road environment. The experiments results shown that the prototype can achieve an
overall accuracy of 88. 42% for vehicle classification. The result of the vehicle classification is affected by
the result of vehicle detection. The vehicle will miss or wrongly classified if the vehicle blob does not fall
in the region of interest. |
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