Terrific Traffic
| dc.contributor.author | Sarmad Khan | |
| dc.contributor.author | FA16-BCS-099 | |
| dc.contributor.author | Dr. Zeeshan Gillani | |
| dc.contributor.author | LHR TP 6158 | |
| dc.date.accessioned | 2026-02-17T11:34:11Z | |
| dc.date.issued | 2020-11-20 | |
| dc.description.abstract | The proposed is a system to detect traffic violations through anomalies detection using Machine Learning techniques from within a live traffic video stream. An anomaly is considered an irregularity or an event which would not occur in normal circumstances. Keeping traffic violations in account the circumstances will be such as Accidents, car crashes and lane violation. The proposed system will be able to differentiate not only between a normal and an abnormal event based on the events in the video stream but will also classify the anomaly accordingly. The purpose of this system is to automate and facilitate the traffic surveillance tasks in daily life. This result is achieved through implementing Image Processing and Deep learning techniques to predict the anomalous section of a video. | |
| dc.identifier.uri | https://repository.cuilahore.edu.pk/handle/123456789/1833 | |
| dc.publisher | Library Information Services, COMSATS University Islamabad, Lahore Campus. | |
| dc.relation.ispartofseries | LHR TP 6158; LHR TP 6158 | |
| dc.subject | Terrific Traffic | |
| dc.subject | TECHNOLOGY::Information technology::Computer science | |
| dc.subject | FA16 | |
| dc.title | Terrific Traffic | |
| dc.type | Thesis |