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Recently, kernelized correlation Filter-based trackers have aroused the interest of many researchers and achieved good results in the field of tracking. However, the current tracking model based on kernelized correlation filters can not deal with the changes of the target appearance and scale effectively. Therefore, in this paper, we intend to solve these two problems and improve the robustness of...
In this paper, we propose a Local Soft Similarity based on Soft Cosine Measure (L3SCM) and then we incorporate it into visual tracking framework. Firstly, we present the soft cosine measure that measures the soft similarity between two vectors of features by taking into consideration similarities of pairs of features. Secondly, we apply this soft similarity in the observation model component of the...
Patch strategy is widely adopted in visual tracking to address partial occlusions. However, most patch-based tracking methods either assume all patches sharing the same importance or exploit simple prior for computing the importance of each patch, which may depress the tracking performance when the target object is non-rigid or the background information is included in the initial bounding box. To...
Developing an effective and efficient appearance model for robust visual tracking is difficult because of various interfering factors, such as postural change, occlusion, and rapid motion. More and more visual tracking methods tend to exploit local sparse appearance model to deal with the above problems. Since in the local sparse appearance model, all individual patch together to form a complete target...
The success of sparse representation, in face recognition and visual tracking, has attracted much attention in computer vision in spite of its computational complexity. These sparse representation-based methods assume that the coding residual follows either Gaussian or Laplacian distribution, which may not be accurate enough to describe the coding residuals in real scenarios. In order to deal with...
Visual object tracking is one of many important applications for surveillance systems. The issues for visual object tracking are robustness from background interference, scaling and occlusion detection. In this paper, visual object tracking using improved Mean Shift algorithm is proposed. Mean Shift algorithm is used to obtain center object target for tracking. Corrected Background Weighted Histogram...
In this paper, we present a novel appearance model using sparse representation and online dictionary learning techniques for visual tracking. In our approach, the visual appearance is represented by sparse representation, and the online dictionary learning strategy is used to adapt the appearance variations during tracking. We unify the sparse representation and online dictionary learning by defining...
In this paper, we propose a new visual tracking algorithm, SRAPF, for object tracking, which is based on sparse representation and annealed particle filter. To find the tracking target at a new frame, each target candidate is sparsely represented by target templates and trivial templates. The sparsity is achieved by solving a l1-regularized least squares problem. After that, Instead of tracking objects...
Sparse representation has been applied to visual tracking by finding the best candidate with minimal reconstruction error using target templates. However most sparse representation based trackers only consider the holistic representation and do not make full use of the sparse coefficients to discriminate between the target and the background, and hence may fail with more possibility when there is...
We present an efficient and robust measurement model for visual tracking. This approach builds on and extends work on measurement model of subspace representation. Subspace-based tracking algorithms have been introduced to visual tracking literature for a decade and show considerable tracking performance due to its robustness in matching. However, the measures used in their measurement models are...
In order to improve the robustness and stability as well as the computation efficiency of the video tracker based on particle filtering, an adaptive state evolution equation and an online increment learning observation likelihood model configured by an updatable eigen-basis of the object appearance subspace is combined into the particle filter to cope with the uncertainties during tracking, and the...
Many object tracking methods based on Adaptive Appearance Models (online learning methods) have been developed in recent years. One problem that can be found with these methods is how to learn variations in object appearance without errors in the image sequence. This paper introduces a novel method, in which a solution to remove learning errors by using an offline learning is proposed; in addition,...
We review some recent techniques for 3D tracking and occlusion handling for computer vision-based augmented reality. We discuss what their limits for real applications are, and why object recognition techniques are certainly the key to further improvements.
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