Suhail Najeeb
Suhail Najeeb
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computer-vision
Traffic Sign Detection under Challenging Conditions
We propose a Traffic Sign Detection & Segmentation pipeline. A faster RCNN has been used to detect traffic signs from different challenged conditions. The challenging conditions are classified using an RCNN. With the help of Kalman filter and Lukas-Kanade tracker the detection process is improved. Finally, a Convolutional Neural Network (CNN) is used to classify the signs of the frames
Shahruk Hossain
,
Suhail Najeeb
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Removal of Artifacts from Vehicle Mounted Images using Convolutional Autoencoders
Artifacts due to environmental and device factors are commonplace while acquiring vehicle mounted images. This project aims to ameliorate the effects of different artifacts like rain, snow and haze on vehicle mounted image sequences which should lead to better performance of computer vision tasks like detection and classification.
Suhail Najeeb
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