3 research outputs found
Visual Tracking via Dynamic Graph Learning
Existing visual tracking methods usually localize a target object with a
bounding box, in which the performance of the foreground object trackers or
detectors is often affected by the inclusion of background clutter. To handle
this problem, we learn a patch-based graph representation for visual tracking.
The tracked object is modeled by with a graph by taking a set of
non-overlapping image patches as nodes, in which the weight of each node
indicates how likely it belongs to the foreground and edges are weighted for
indicating the appearance compatibility of two neighboring nodes. This graph is
dynamically learned and applied in object tracking and model updating. During
the tracking process, the proposed algorithm performs three main steps in each
frame. First, the graph is initialized by assigning binary weights of some
image patches to indicate the object and background patches according to the
predicted bounding box. Second, the graph is optimized to refine the patch
weights by using a novel alternating direction method of multipliers. Third,
the object feature representation is updated by imposing the weights of patches
on the extracted image features. The object location is predicted by maximizing
the classification score in the structured support vector machine. Extensive
experiments show that the proposed tracking algorithm performs well against the
state-of-the-art methods on large-scale benchmark datasets.Comment: Submitted to TPAMI 201
Learning Compact Target-Oriented Feature Representations for Visual Tracking
Many state-of-the-art trackers usually resort to the pretrained convolutional
neural network (CNN) model for correlation filtering, in which deep features
could usually be redundant, noisy and less discriminative for some certain
instances, and the tracking performance might thus be affected. To handle this
problem, we propose a novel approach, which takes both advantages of good
generalization of generative models and excellent discrimination of
discriminative models, for visual tracking. In particular, we learn compact,
discriminative and target-oriented feature representations using the Laplacian
coding algorithm that exploits the dependence among the input local features in
a discriminative correlation filter framework. The feature representations and
the correlation filter are jointly learnt to enhance to each other via a fast
solver which only has very slight computational burden on the tracking speed.
Extensive experiments on three benchmark datasets demonstrate that this
proposed framework clearly outperforms baseline trackers with a modest impact
on the frame rate, and performs comparably against the state-of-the-art
methods.Comment: 10 pages, 4 figures,6 table
Edge-guided Non-local Fully Convolutional Network for Salient Object Detection
Fully Convolutional Neural Network (FCN) has been widely applied to salient
object detection recently by virtue of high-level semantic feature extraction,
but existing FCN based methods still suffer from continuous striding and
pooling operations leading to loss of spatial structure and blurred edges. To
maintain the clear edge structure of salient objects, we propose a novel
Edge-guided Non-local FCN (ENFNet) to perform edge guided feature learning for
accurate salient object detection. In a specific, we extract hierarchical
global and local information in FCN to incorporate non-local features for
effective feature representations. To preserve good boundaries of salient
objects, we propose a guidance block to embed edge prior knowledge into
hierarchical feature maps. The guidance block not only performs feature-wise
manipulation but also spatial-wise transformation for effective edge
embeddings. Our model is trained on the MSRA-B dataset and tested on five
popular benchmark datasets. Comparing with the state-of-the-art methods, the
proposed method achieves the best performance on all datasets.Comment: 10 pages, 6 figure