Traffic Congestion Prediction using Deep Convolutional  Neural Networks: A Color-coding Approach

Ishraque, Imrez; Hasan, Md. Sumit; Al-Amin, Md. Sifath

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dc.contributor.author	Ishraque, Imrez
dc.contributor.author	Hasan, Md. Sumit
dc.contributor.author	Al-Amin, Md. Sifath
dc.date.accessioned	2024-01-17T07:01:49Z
dc.date.available	2024-01-17T07:01:49Z
dc.date.issued	2023-04-30
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dc.identifier.uri	http://hdl.handle.net/123456789/2039
dc.description	Supervised by Mr. Mirza Fuad Adnan, Assistant Professor, Department of Electrical and Electronics Engineering (EEE) Islamic University of Technology (IUT) Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.description.abstract	Traffic video data has become a critical factor in limiting traffic congestion due to recent advancements in computer vision. This work proposes a unique technique for traffic video classification using a color-coding scheme before training the traffic data in a deep convolutional neural network. At first, the video data is transformed into an imagery data set, and vehicle detection is performed using the You Only Look Once algorithm. A color-coded scheme has been adopted to transform the imagery dataset into a binary image dataset. These binary images are fed to a deep convolutional network. Using the UCSD dataset, we have obtained a classification accuracy of 98.2%	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	Traffic Congestion Prediction using Deep Convolutional Neural Networks: A Color-coding Approach	en_US
dc.type	Thesis	en_US