TRAFFIC VIOLATION DETECTION WITH COMPUTER VISION

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dc.contributor.authorDAHAL, NEHA
dc.contributor.authorG.C., SIMON
dc.contributor.authorMAINALI, SUBASH
dc.contributor.authorSUBEDI, UDAYA RAJ
dc.date.accessioned2023-07-31T10:11:00Z
dc.date.available2023-07-31T10:11:00Z
dc.date.issued2023-04
dc.descriptionIn this project we used YOLOv5s that was trained on custom dataset collected by us which consisted of 2193 images of 6 classes which was augmented to extend our dataset to 5259 images and was split in the ratio of 70:20:10 for train, validation, and test respectively.en_US
dc.description.abstractIn this project we used YOLOv5s that was trained on custom dataset collected by us which consisted of 2193 images of 6 classes which was augmented to extend our dataset to 5259 images and was split in the ratio of 70:20:10 for train, validation, and test respectively. For tracking the detected objects in the video, we used DeepSORT which tracks and outputs the bounding box for the object with respective track IDs. Then if the detected and tracked object have violated traffic lights the corresponding license plate of the object in question is sent as input for segmentation program. The image of the license plate undergoes HSV color space conversion, color masking and perspective transformed in that order before it is preprocessed for profiling the different types of license plate in the dataset. The image undergoes horizontal projection profiling and vertical projection profiling which is then validated to separate the characters of the license plate.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14540/18865
dc.language.isoenen_US
dc.publisherI.O.E. Pulchowk Campusen_US
dc.subjectcharacter segmentation,en_US
dc.subjecttracking,en_US
dc.subjectDeepSORT,en_US
dc.titleTRAFFIC VIOLATION DETECTION WITH COMPUTER VISIONen_US
dc.typeReporten_US
local.academic.levelBacheloren_US
local.affiliatedinstitute.titlePulchowk Campusen_US
local.institute.titleInstitute of Engineeringen_US
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