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Kernelized Correlation Filter has shown the unprecedented powerful discriminability of non-linear kernels. However, most of state-of-the-art methods ignore the interaction between channels and multi-kernel. Furthermore, the compressed kernel with simple ...

Similar object interference (SOI) problem challenges the single object tracking (SOT) task, leading to the failure of feature-based trackers and subsequent performance degradation. Unfortunately, current generic SOT benchmarks do not effectively tackle ...

The problem of background clutter (BC) is caused by distractors in the background that resemble the target’s appearance, thereby reducing the precision of visual trackers. We consider these similar distractors as noise and formulate a denoising ...

In recent years, many advanced visual object tracking algorithms have achieved significant performance improvements in daytime scenes. However, when these algorithms are applied at night on unmanned aerial vehicles, they often exhibit poor ...

Robust visual tracking of instruments is an important task in retinal microsurgery. In this context, the instruments are subject to a large variety of appearance changes due to illumination and other changes during a procedure, which makes the ...

With the evolution of robotic systems, unmanned aerial vehicles (UAV) have become a target of interest for domains such as computer vision (CV) and artificial intelligence (AI), contributing to a variety of applications for surveillance, transportation ...

We present a novel noise-injected Markov chain Monte Carlo (NMCMC) method for visual tracking, which enables fast convergence through adversarial attacks. The proposed NMCMC consists of three steps: noise-injected proposal, acceptance, and ...

The boundary effect of correlation filters is one key issue to limit the performance of visual tracking. Most existing methods focus on using regularization to constrain filters in the spatial domain, but less attention is paid to the ...

Most existing scale solutions of Correlation Filter-based trackers fail to consider the priority of target scale calculation. Their high complexity destroys the high speed performance of the tracking method. To tackle this problem, an optimization ...

Visual tracking is an art of tracking a moving object over video frames using non-stationary cameras, for which feature descriptors of the target are computed and applied to a motion model. In this paper, SURF is used to compute the feature ...

In recent years, Convolutional Neural Network (CNN) based trackers have achieved state-of-the-art performance on multiple benchmark datasets. Most of these trackers train a binary classifier to distinguish the target from its background. However, they ...

Efficient tracking of heavy loads is the overall goal for overhead cranes in the workplace. This article presents a new way to simply and effectively track overhead cranes at higher speeds. A real-time tracking method is presented here to make ...

The authors attempted to construct a novel sensor networking system that estimates locations of sensor nodes as locations of humans wearing them via image processing. In this application, computationally efficient human localization is indispensable ...

The strong interest in data-driven surgical procedures has motivated the need for automatically analyzing instrument trajectories during surgical procedures to improve the efficacy of modern robot-assisted surgeries. We proposed a single view camera-...

End-to-end Correlation Filters based trackers have shown excellent performance in visual tracking both in speed and accuracy. Most existing approaches adopt the Convolutional Neural Network (CNN) features pre-trained from the large-scale datasets off-...

In this paper, we present a novel visual object tracking model via multi-view semi-supervised learning. Instead of concatenating multiple views into a single view directly to adapt to conventional machine learning algorithms, the combination of views is ...

Visual tracking arises in various real-world tasks where an object should be located in a video. Sparse representation can implement tracking problems by linearly representing object with a few templates. However, this approach has two main ...

We present a biologically inspired long-term object tracking system based on Virtual Generalizing Random Access Memory (VG-RAM) Weightless Neural Networks (WNN). VG-RAM WNN is an effective machine learning technique that offers simple implementation and ...

In this paper, we present a novel online visual tracking method based on linear representation. First, we present a novel probability continuous outlier model (PCOM) to depict the continuous outliers that occur in the linear representation model. In the ...

Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object recognition and ...