Scene Change Detection
Typ
Examensarbete för masterexamen
Program
Publicerad
2022
Författare
Siddhant, Som
Swaathy, Sambath
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
Detection, pixel-level, slow, annotating