Adaptive Sample Selection Strategy for Training Object Trackers
- Title
- Adaptive Sample Selection Strategy for Training Object Trackers
- Authors
- 이승호
- Date Issued
- 2024
- Abstract
- Most visual object tracker (VOT) models are trained in a supervised manner,
and their performance is completely dependent on the quality of data labels.
However, the sample selection criteria of existing region proposal network based
trackers use heuristic rules and do not consider object characteristics of individual
images. This hinders learning semantic features at the data level.
In this thesis, we propose a training sample selection strategy using semantic
mask for visual object tracker. The proposed algorithm selects balanced training
samples by considering the shape of the object and the distribution of the
candidate samples. Our method achieved 0.475 EAO on the VOT evaluation
dataset and a success rate of 0.510 on the LaSOT benchmark, which exceeded
the performance of the baseline model SiamRPN++.
- URI
- http://postech.dcollection.net/common/orgView/200000735302
https://oasis.postech.ac.kr/handle/2014.oak/123389
- Article Type
- Thesis
- Files in This Item:
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