TRAFFIC VIOLATION DETECTION WITH COMPUTER VISION
Date
2023-04
Journal Title
Journal ISSN
Volume Title
Publisher
I.O.E. Pulchowk Campus
Abstract
In 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.
Description
In 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.
Keywords
character segmentation,, tracking,, DeepSORT,