1,039 research outputs found
Remove Cosine Window from Correlation Filter-based Visual Trackers: When and How
Correlation filters (CFs) have been continuously advancing the
state-of-the-art tracking performance and have been extensively studied in the
recent few years. Most of the existing CF trackers adopt a cosine window to
spatially reweight base image to alleviate boundary discontinuity. However,
cosine window emphasizes more on the central region of base image and has the
risk of contaminating negative training samples during model learning. On the
other hand, spatial regularization deployed in many recent CF trackers plays a
similar role as cosine window by enforcing spatial penalty on CF coefficients.
Therefore, we in this paper investigate the feasibility to remove cosine window
from CF trackers with spatial regularization. When simply removing cosine
window, CF with spatial regularization still suffers from small degree of
boundary discontinuity. To tackle this issue, binary and Gaussian shaped mask
functions are further introduced for eliminating boundary discontinuity while
reweighting the estimation error of each training sample, and can be
incorporated with multiple CF trackers with spatial regularization. In
comparison to the counterparts with cosine window, our methods are effective in
handling boundary discontinuity and sample contamination, thereby benefiting
tracking performance. Extensive experiments on three benchmarks show that our
methods perform favorably against the state-of-the-art trackers using either
handcrafted or deep CNN features. The code is publicly available at
https://github.com/lifeng9472/Removing_cosine_window_from_CF_trackers.Comment: 13 pages, 7 figures, submitted to IEEE Transactions on Image
Processin
Deformable Object Tracking with Gated Fusion
The tracking-by-detection framework receives growing attentions through the
integration with the Convolutional Neural Networks (CNNs). Existing
tracking-by-detection based methods, however, fail to track objects with severe
appearance variations. This is because the traditional convolutional operation
is performed on fixed grids, and thus may not be able to find the correct
response while the object is changing pose or under varying environmental
conditions. In this paper, we propose a deformable convolution layer to enrich
the target appearance representations in the tracking-by-detection framework.
We aim to capture the target appearance variations via deformable convolution,
which adaptively enhances its original features. In addition, we also propose a
gated fusion scheme to control how the variations captured by the deformable
convolution affect the original appearance. The enriched feature representation
through deformable convolution facilitates the discrimination of the CNN
classifier on the target object and background. Extensive experiments on the
standard benchmarks show that the proposed tracker performs favorably against
state-of-the-art methods
- …