Name
Abstract | ||
|---|---|---|
Change detection is an important task in computer vision and video processing. Due to unimportant or nuisance forms of change, traditional methods require sophisticated image pre-processing and possibly manual interaction. In this work, we propose an end-to-end approach for change detection to identify temporal changes in multiple images. Our approach feeds a pair of images into a deep convolutional neural network combining the model of MatchNet [1] and the Fully Convolutional Network [2] modified to reduce the number of parameters. We train and evaluate the proposed approach using a subset of frames from the Change Detection challenge 2014 dataset (CDnet 2014). Experimental evaluation comparing the performance of the proposed approach with several known approaches shows that the proposed approach outperforms existing methods. |
Year | Venue | Keywords |
|---|---|---|
2018 | 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | Change detection, deep neural network, MatchNet, FCN, MFCNet |
Field | DocType | ISSN |
Computer vision,Video processing,Change detection,Pattern recognition,Task analysis,End-to-end principle,Convolutional neural network,Computer science,Image segmentation,Feature extraction,Preprocessor,Artificial intelligence | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||