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Conference Paper
Learning Video Correspondence using Appearance Module for Target Tracking
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- Authors
- Jin-mo Choi, Jeany Son, Sangjoon Park
- Issue Date
- 2021-01
- Citation
- International Conference on Big Data and Smart Computing (BigComp) 2021, pp.287-290
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- We introduce a new method for self-supervised video correspondence matching to effectively track targets in battlefield situations. Specifically, we propose Appearance module that enforces to maintain a target appearance between two consecutive frames in a local window, where an affinity matrix is computed on high-resolution feature maps with a small search window. It has an advantage of mitigating an over-fitting problem of a conventional affinity matrix that only predicts motions by reducing ambiguity of labels in a pretest task. Also the proposed method preserves the detailed shape of an object by handling high resolution information while high computational costs due to the correlation filter is alleviated by a small search window. Our experimental results on the DAVIS2017 dataset showed the significant performance improvement over the baseline.
- KSP Keywords
- Correlation Filter, Correspondence Matching, Feature map, High resolution, Search Window, affinity matrix, computational cost, fitting problem, local window, new method, over-fitting