21,171 research outputs found
Robust and Real-time Deep Tracking Via Multi-Scale Domain Adaptation
Visual tracking is a fundamental problem in computer vision. Recently, some
deep-learning-based tracking algorithms have been achieving record-breaking
performances. However, due to the high complexity of deep learning, most deep
trackers suffer from low tracking speed, and thus are impractical in many
real-world applications. Some new deep trackers with smaller network structure
achieve high efficiency while at the cost of significant decrease on precision.
In this paper, we propose to transfer the feature for image classification to
the visual tracking domain via convolutional channel reductions. The channel
reduction could be simply viewed as an additional convolutional layer with the
specific task. It not only extracts useful information for object tracking but
also significantly increases the tracking speed. To better accommodate the
useful feature of the target in different scales, the adaptation filters are
designed with different sizes. The yielded visual tracker is real-time and also
illustrates the state-of-the-art accuracies in the experiment involving two
well-adopted benchmarks with more than 100 test videos.Comment: 6 page
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
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