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Deep learning for visual tracking: A comprehensive survey
Visual target tracking is one of the most sought-after yet challenging research topics in
computer vision. Given the ill-posed nature of the problem and its popularity in a broad …
computer vision. Given the ill-posed nature of the problem and its popularity in a broad …
Satellite video single object tracking: A systematic review and an oriented object tracking benchmark
Single object tracking (SOT) in satellite video (SV) enables the continuous acquisition of
position and range information of an arbitrary object, showing promising value in remote …
position and range information of an arbitrary object, showing promising value in remote …
Siamrpn++: Evolution of siamese visual tracking with very deep networks
Siamese network based trackers formulate tracking as convolutional feature cross-
correlation between target template and searching region. However, Siamese trackers still …
correlation between target template and searching region. However, Siamese trackers still …
AutoTrack: Towards high-performance visual tracking for UAV with automatic spatio-temporal regularization
Most existing trackers based on discriminative correlation filters (DCF) try to introduce
predefined regularization term to improve the learning of target objects, eg, by suppressing …
predefined regularization term to improve the learning of target objects, eg, by suppressing …
Visual object tracking with discriminative filters and siamese networks: a survey and outlook
Accurate and robust visual object tracking is one of the most challenging and fundamental
computer vision problems. It entails estimating the trajectory of the target in an image …
computer vision problems. It entails estimating the trajectory of the target in an image …
SCSTCF: spatial-channel selection and temporal regularized correlation filters for visual tracking
J Zhang, W Feng, T Yuan, J Wang, AK Sangaiah - Applied Soft Computing, 2022 - Elsevier
Recently, combining multiple features into discriminative correlation filters to improve
tracking representation has shown great potential in object tracking. Existing trackers apply …
tracking representation has shown great potential in object tracking. Existing trackers apply …
Learning adaptive spatial-temporal context-aware correlation filters for UAV tracking
Tracking in the unmanned aerial vehicle (UAV) scenarios is one of the main components of
target-tracking tasks. Different from the target-tracking task in the general scenarios, the …
target-tracking tasks. Different from the target-tracking task in the general scenarios, the …
Target-aware deep tracking
Existing deep trackers mainly use convolutional neural networks pre-trained for the generic
object recognition task for representations. Despite demonstrated successes for numerous …
object recognition task for representations. Despite demonstrated successes for numerous …
Visual tracking via adaptive spatially-regularized correlation filters
In this work, we propose a novel adaptive spatially-regularized correlation filters (ASRCF)
model to simultaneously optimize the filter coefficients and the spatial regularization weight …
model to simultaneously optimize the filter coefficients and the spatial regularization weight …
Learning spatial-temporal regularized correlation filters for visual tracking
Abstract Discriminative Correlation Filters (DCF) are efficient in visual tracking but suffer from
unwanted boundary effects. Spatially Regularized DCF (SRDCF) has been suggested to …
unwanted boundary effects. Spatially Regularized DCF (SRDCF) has been suggested to …