A Novel Bounding Box Regression Method for Single Object Tracking
CoRR(2024)
Abstract
Locating an object in a sequence of frames, given its appearance in the first
frame of the sequence, is a hard problem that involves many stages. Usually,
state-of-the-art methods focus on bringing novel ideas in the visual encoding
or relational modelling phases. However, in this work, we show that bounding
box regression from learned joint search and template features is of high
importance as well. While previous methods relied heavily on well-learned
features representing interactions between search and template, we hypothesize
that the receptive field of the input convolutional bounding box network plays
an important role in accurately determining the object location. To this end,
we introduce two novel bounding box regression networks: inception and
deformable. Experiments and ablation studies show that our inception module
installed on the recent ODTrack outperforms the latter on three benchmarks: the
GOT-10k, the UAV123 and the OTB2015.
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