300 research outputs found
End-to-end Flow Correlation Tracking with Spatial-temporal Attention
Discriminative correlation filters (DCF) with deep convolutional features
have achieved favorable performance in recent tracking benchmarks. However,
most of existing DCF trackers only consider appearance features of current
frame, and hardly benefit from motion and inter-frame information. The lack of
temporal information degrades the tracking performance during challenges such
as partial occlusion and deformation. In this work, we focus on making use of
the rich flow information in consecutive frames to improve the feature
representation and the tracking accuracy. Firstly, individual components,
including optical flow estimation, feature extraction, aggregation and
correlation filter tracking are formulated as special layers in network. To the
best of our knowledge, this is the first work to jointly train flow and
tracking task in a deep learning framework. Then the historical feature maps at
predefined intervals are warped and aggregated with current ones by the guiding
of flow. For adaptive aggregation, we propose a novel spatial-temporal
attention mechanism. Extensive experiments are performed on four challenging
tracking datasets: OTB2013, OTB2015, VOT2015 and VOT2016, and the proposed
method achieves superior results on these benchmarks.Comment: Accepted in CVPR 201
Learning feed-forward one-shot learners
One-shot learning is usually tackled by using generative models or
discriminative embeddings. Discriminative methods based on deep learning, which
are very effective in other learning scenarios, are ill-suited for one-shot
learning as they need large amounts of training data. In this paper, we propose
a method to learn the parameters of a deep model in one shot. We construct the
learner as a second deep network, called a learnet, which predicts the
parameters of a pupil network from a single exemplar. In this manner we obtain
an efficient feed-forward one-shot learner, trained end-to-end by minimizing a
one-shot classification objective in a learning to learn formulation. In order
to make the construction feasible, we propose a number of factorizations of the
parameters of the pupil network. We demonstrate encouraging results by learning
characters from single exemplars in Omniglot, and by tracking visual objects
from a single initial exemplar in the Visual Object Tracking benchmark.Comment: The first three authors contributed equally, and are listed in
alphabetical orde
SiamLST: Learning Spatial and Channel-wise Transform for Visual Tracking
Siamese network based trackers regard visual tracking as a similarity matching task between the target template and search region patches, and achieve a good balance between accuracy and speed in recent years. However, existing trackers do not effectively exploit the spatial and inter-channel cues, which lead to the redundancy of pre-trained model parameters. In this paper, we design a novel visual tracker based on a Learnable Spatial and Channel-wise Transform in Siamese network (SiamLST). The SiamLST tracker includes a powerful feature extraction backbone and an efficient cross-correlation method. The proposed algorithm takes full advantages of CNN and the learnable sparse transform module to represent the template and search patches, which effectively exploit the spatial and channel-wise correlations to deal with complicated scenarios, such as motion blur, in-plane rotation and partial occlusion. Experimental results conducted on multiple tracking benchmarks including OTB2015, VOT2016, GOT-10k and VOT2018 demonstrate that the proposed SiamLST has excellent tracking performances
Learning Spatial-Aware Regressions for Visual Tracking
In this paper, we analyze the spatial information of deep features, and
propose two complementary regressions for robust visual tracking. First, we
propose a kernelized ridge regression model wherein the kernel value is defined
as the weighted sum of similarity scores of all pairs of patches between two
samples. We show that this model can be formulated as a neural network and thus
can be efficiently solved. Second, we propose a fully convolutional neural
network with spatially regularized kernels, through which the filter kernel
corresponding to each output channel is forced to focus on a specific region of
the target. Distance transform pooling is further exploited to determine the
effectiveness of each output channel of the convolution layer. The outputs from
the kernelized ridge regression model and the fully convolutional neural
network are combined to obtain the ultimate response. Experimental results on
two benchmark datasets validate the effectiveness of the proposed method.Comment: To appear in CVPR201
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