Scene Change Detection
| dc.contributor.author | Siddhant, Som | |
| dc.contributor.author | Swaathy, Sambath | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för matematiska vetenskaper | sv |
| dc.contributor.examiner | Persson, Daniel | |
| dc.contributor.supervisor | Schauer, Moritz | |
| dc.contributor.supervisor | Norström, Kristofer | |
| dc.date.accessioned | 2022-06-29T14:26:19Z | |
| dc.date.available | 2022-06-29T14:26:19Z | |
| dc.date.issued | 2022 | sv |
| dc.date.submitted | 2020 | |
| dc.description.abstract | Scene Change Detection (SCD) identifies changes between the images taken at two different times, using pixel-level or point cloud approaches in most cases. Training a neural network for such a task requires a large number of images with annotated changes. Annotating changes is a slow, costly and time-consuming process. The state-of-the-art (SOTA) approach for SCD, like the DR-TANet paper, is based on transfer learning from large ImageNet datasets. This is a supervised technique and to overcome the challenges mentioned above, we introduce a self-supervised pretraining method with unlabeled datasets based on a existing D-SSCD approach that learns temporal-consistent representations of a pair of images. This project is an investigation of these approaches that can train and evaluate on available datasets through the use of a suitable loss function for the purpose of SCD. We compare results for different percentages of labeled data from different models and benchmark datasets such as Visual Localization CMU (VL_CMU_CD) and Panoramic change detection (PCD) datasets. | sv |
| dc.identifier.coursecode | MVEX03 | sv |
| dc.identifier.uri | https://hdl.handle.net/20.500.12380/304951 | |
| dc.language.iso | eng | sv |
| dc.setspec.uppsok | PhysicsChemistryMaths | |
| dc.subject | Detection, pixel-level, slow, annotating | sv |
| dc.title | Scene Change Detection | sv |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.uppsok | H |