3,355 research outputs found
Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters
Standard RGB-D trackers treat the target as an inherently 2D structure, which
makes modelling appearance changes related even to simple out-of-plane rotation
highly challenging. We address this limitation by proposing a novel long-term
RGB-D tracker - Object Tracking by Reconstruction (OTR). The tracker performs
online 3D target reconstruction to facilitate robust learning of a set of
view-specific discriminative correlation filters (DCFs). The 3D reconstruction
supports two performance-enhancing features: (i) generation of accurate spatial
support for constrained DCF learning from its 2D projection and (ii) point
cloud based estimation of 3D pose change for selection and storage of
view-specific DCFs which are used to robustly localize the target after
out-of-view rotation or heavy occlusion. Extensive evaluation of OTR on the
challenging Princeton RGB-D tracking and STC Benchmarks shows it outperforms
the state-of-the-art by a large margin
Recent Advances of Local Mechanisms in Computer Vision: A Survey and Outlook of Recent Work
Inspired by the fact that human brains can emphasize discriminative parts of
the input and suppress irrelevant ones, substantial local mechanisms have been
designed to boost the development of computer vision. They can not only focus
on target parts to learn discriminative local representations, but also process
information selectively to improve the efficiency. In terms of application
scenarios and paradigms, local mechanisms have different characteristics. In
this survey, we provide a systematic review of local mechanisms for various
computer vision tasks and approaches, including fine-grained visual
recognition, person re-identification, few-/zero-shot learning, multi-modal
learning, self-supervised learning, Vision Transformers, and so on.
Categorization of local mechanisms in each field is summarized. Then,
advantages and disadvantages for every category are analyzed deeply, leaving
room for exploration. Finally, future research directions about local
mechanisms have also been discussed that may benefit future works. To the best
our knowledge, this is the first survey about local mechanisms on computer
vision. We hope that this survey can shed light on future research in the
computer vision field
- …