Data representativity is crucial when drawing inference from data through machine learning
models. Scholars have increased focus on unraveling the bias and fairness in models, also …

These days online networking is generally utilized as the wellspring of data as a result of its
ease, simple to get to nature. In any case, expending news from online life is a twofold …

Discriminative correlation filters (DCFs) have been widely used in the tracking community
recently. DCFs-based trackers utilize samples generated by circularly shifting from an image …

Common tracking algorithms only use a single feature to describe the target appearance,
which makes the appearance model easily disturbed by noise. Furthermore, the tracking …

Visual object tracking with semantic deep features has recently attracted much attention in
computer vision. Especially, Siamese trackers, which aim to learn a decision making-based …

Most of the correlation filter based tracking algorithms can achieve good performance and
maintain fast computational speed. However, in some complicated tracking scenes, there is …

Most current multi-object trackers focus on short-term tracking, and are based on deep and
complex systems that often cannot operate in real-time, making them impractical for video …

Imbalanced learning has become a research emphasis in recent years because of the
growing number of class-imbalance classification problems in real applications. It is …

In a visual object tracking technology the particle filter (PF) is frequently used. The main
drawback of the particle filter is that a large quantity of particles is required. This paper …

Long-term visual tracking undergoes more challenges and is closer to realistic applications
than short-term tracking. However, the performances of most existing methods have been …