20,421 research outputs found
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
Discriminative Correlation Filters (DCF) have demonstrated excellent
performance for visual object tracking. The key to their success is the ability
to efficiently exploit available negative data by including all shifted
versions of a training sample. However, the underlying DCF formulation is
restricted to single-resolution feature maps, significantly limiting its
potential. In this paper, we go beyond the conventional DCF framework and
introduce a novel formulation for training continuous convolution filters. We
employ an implicit interpolation model to pose the learning problem in the
continuous spatial domain. Our proposed formulation enables efficient
integration of multi-resolution deep feature maps, leading to superior results
on three object tracking benchmarks: OTB-2015 (+5.1% in mean OP), Temple-Color
(+4.6% in mean OP), and VOT2015 (20% relative reduction in failure rate).
Additionally, our approach is capable of sub-pixel localization, crucial for
the task of accurate feature point tracking. We also demonstrate the
effectiveness of our learning formulation in extensive feature point tracking
experiments. Code and supplementary material are available at
http://www.cvl.isy.liu.se/research/objrec/visualtracking/conttrack/index.html.Comment: Accepted at ECCV 201
Good Features to Correlate for Visual Tracking
During the recent years, correlation filters have shown dominant and
spectacular results for visual object tracking. The types of the features that
are employed in these family of trackers significantly affect the performance
of visual tracking. The ultimate goal is to utilize robust features invariant
to any kind of appearance change of the object, while predicting the object
location as properly as in the case of no appearance change. As the deep
learning based methods have emerged, the study of learning features for
specific tasks has accelerated. For instance, discriminative visual tracking
methods based on deep architectures have been studied with promising
performance. Nevertheless, correlation filter based (CFB) trackers confine
themselves to use the pre-trained networks which are trained for object
classification problem. To this end, in this manuscript the problem of learning
deep fully convolutional features for the CFB visual tracking is formulated. In
order to learn the proposed model, a novel and efficient backpropagation
algorithm is presented based on the loss function of the network. The proposed
learning framework enables the network model to be flexible for a custom
design. Moreover, it alleviates the dependency on the network trained for
classification. Extensive performance analysis shows the efficacy of the
proposed custom design in the CFB tracking framework. By fine-tuning the
convolutional parts of a state-of-the-art network and integrating this model to
a CFB tracker, which is the top performing one of VOT2016, 18% increase is
achieved in terms of expected average overlap, and tracking failures are
decreased by 25%, while maintaining the superiority over the state-of-the-art
methods in OTB-2013 and OTB-2015 tracking datasets.Comment: Accepted version of IEEE Transactions on Image Processin
SANet: Structure-Aware Network for Visual Tracking
Convolutional neural network (CNN) has drawn increasing interest in visual
tracking owing to its powerfulness in feature extraction. Most existing
CNN-based trackers treat tracking as a classification problem. However, these
trackers are sensitive to similar distractors because their CNN models mainly
focus on inter-class classification. To address this problem, we use
self-structure information of object to distinguish it from distractors.
Specifically, we utilize recurrent neural network (RNN) to model object
structure, and incorporate it into CNN to improve its robustness to similar
distractors. Considering that convolutional layers in different levels
characterize the object from different perspectives, we use multiple RNNs to
model object structure in different levels respectively. Extensive experiments
on three benchmarks, OTB100, TC-128 and VOT2015, show that the proposed
algorithm outperforms other methods. Code is released at
http://www.dabi.temple.edu/~hbling/code/SANet/SANet.html.Comment: In CVPR Deep Vision Workshop, 201
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
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