Semantic Scene Change Detection: Evaluation through Classical & Machine Learning Algorithms
| dc.contributor.author | Sreekumar, Jithinraj | |
| dc.contributor.author | Desai, Shreya | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för data och informationsteknik | sv |
| dc.contributor.examiner | Seger, Carl-Johan | |
| dc.contributor.supervisor | Damaschke, Peter | |
| dc.date.accessioned | 2022-05-17T09:36:27Z | |
| dc.date.available | 2022-05-17T09:36:27Z | |
| dc.date.issued | 2021 | sv |
| dc.date.submitted | 2020 | |
| dc.description.abstract | Scene change detection helps to detect changes in a pair of multitemporal images of the same scene. We apply the concept of scene change detection to detect misplaced objects in a passenger vehicle. Deep learning neural networks have been extensively used in scene change detection. We study scene change detection using the classical Watershed algorithm and machine learning algorithms. In machine learning, we exploit the feature extraction capability of ResNet and Spatial Pyramid Pooling to predict the scene change. The performance of the classical and machine learning algorithms are also compared. The models are trained on a custom dataset and evaluated using the metrics, dice coefficient, mean intersection over union (mIoU) and pixel accuracy. We infer that the machine learning model significantly outperforms the classical model in terms of mIoU score. | sv |
| dc.identifier.coursecode | DATX05 | sv |
| dc.identifier.uri | https://hdl.handle.net/20.500.12380/304578 | |
| dc.language.iso | eng | sv |
| dc.setspec.uppsok | Technology | |
| dc.subject | scene change detection | sv |
| dc.subject | machine learning | sv |
| dc.subject | semantic segmentation | sv |
| dc.subject | convolutional neural network | sv |
| dc.subject | residual neural network | sv |
| dc.subject | siamese network | sv |
| dc.subject | spatial pyramid pooling | sv |
| dc.title | Semantic Scene Change Detection: Evaluation through Classical & Machine Learning Algorithms | sv |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.uppsok | H |
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