In recent years, visual object tracking has become a very active research area. An
increasing number of tracking algorithms are being proposed each year. It is because …

Recently, discriminatively learned correlation filters (DCF) has attracted much attention in
visual object tracking community. The success of DCF is potentially attributed to the fact that …

Learning a large-scale regression model has proven to be one of the most successful
approaches for visual tracking as in recent correlation filter (CF)-based trackers. Different …

Correlation filters (CF), a popular tool for visual tracking, suffer from unwanted boundary
effects due to the periodic assumption needed for FFT implementation. To address this …

Object tracking in a real-life scenario is very challenging due to occlusion. State-space
models like Kalman and particle filters are well known to handle such a particular problem …

Recent progresses in visual tracking have greatly improved the tracking performance.
However, challenges such as occlusion and view change remain obstacles in real world …

Visual object tracking is as a critical function for many computer vision tasks such as motion
analysis, event detection and action recognition. Recently, Siamese network based trackers …

Correlation-filter-based trackers have shown favorable accuracy and efficiency in visual
tracking. However, most of these trackers are prone to drift in cases of heavy occlusions and …

We herein propose a novel visual tracking approach using cascaded discriminative
correlation filters (DCFs). The approach consists of two stages. In the first stage, a DCF is …

We propose an object tracking method under large motion in image sequences. Dense
sampling and particle filtering have been widely applied to cope with this problem; however …