Dynamic object detection using moving cameras for UAV collision prevention
| dc.contributor.author | Dadrass, Arman | |
| dc.contributor.supervisor | Baniasadi, Amirali | |
| dc.date.accessioned | 2024-09-06T21:38:42Z | |
| dc.date.available | 2024-09-06T21:38:42Z | |
| dc.date.issued | 2024 | |
| dc.degree.department | Department of Electrical and Computer Engineering | |
| dc.degree.level | Master of Engineering MEng | |
| dc.description.abstract | In this project, the focus is on object and motion detection in video streams from a camera mounted on a moving platform, such as a drone. Motion segmentation and moving object detection are fundamental for object tracking and collision avoidance in autonomous vehicles and flying drones. Moving object detection and tracking with a moving camera is challenging due to the combined effects of the camera’s motion relative to its mounting base and the movement of the platform within the environment. The limited field of view of the camera results in frequent object discontinuity and severe background variations, which conventional background subtraction approaches for fixed cameras cannot handle. To address these challenges, dense optical flow clustering is used to detect moving objects with a moving camera. The clusters correspond one-to-one with moving objects and background motion due to camera's motion; however, objects frequently enter or leave the scene, necessitating frequent redefinition and recalculation of clusters. Additionally, since the number of objects in the scene is unknown and can vary over time, the clustering algorithm must adapt quickly to changing scenarios. Therefore, the Adaptive Resonance Theory-2 (ART2) network was adapted to eliminate the need for pre-tuning the number of clusters as a hyper-parameter, automating the process similarly to human perception and enabling rapid redefinition and recalculation of varying cluster numbers. The performance of the proposed approach was evaluated in terms of execution time and accuracy using the VisDrone and KITTI datasets, and the results are discussed. | |
| dc.description.scholarlevel | Graduate | |
| dc.identifier.uri | https://hdl.handle.net/1828/20387 | |
| dc.language.iso | en | |
| dc.rights | Available to the World Wide Web | |
| dc.subject | Moving object detection with moving cameras | |
| dc.subject | ART2 | |
| dc.subject | optical flow | |
| dc.title | Dynamic object detection using moving cameras for UAV collision prevention | |
| dc.type | project |