Publication:
Near-Miss Traffic Trajectory Detection Based On Deep Learning

Simple item page
dc.contributor.authorYang, Lu
dc.date.accessioned2026-05-07T04:26:08Z
dc.date.available2026-05-07T04:26:08Z
dc.date.issued2025-02
dc.description.abstractComputer vision-based methods have indeed been widely employed for monitoring road traffic conditions. Traffic safety is a critical concern in urban environments, with near-miss events serving as valuable indicators of potential accidents. In this research, an innovative framework is proposed that combines yolov7 with transformer-based structures and segmentation techniques for robust object detection, tracking, and near-miss event analysis in traffic scenarios. Utilizing the real-time object detection capabilities of yolov7, it is augmented through the integration of transformer architectures. This enhancement enables the capture of longrange dependencies and contextual information, thereby improving accuracy in object recognition and localization. Additionally, segmentation methods are employed to delineate objects within the scene, further refining the detection box to better fit the target object.
dc.identifier.urihttps://erepo.usm.my/handle/123456789/24128
dc.language.isoen
dc.subjectTraffic safety
dc.titleNear-Miss Traffic Trajectory Detection Based On Deep Learning
dc.typeResource Types::text::thesis::doctoral thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
Files
Collections
Pusat Pengajian Sains Komputer - Tesis