Recently, sparse coding has been successfully applied in visual tracking. The goal of this
paper is to review the state-of-the-art tracking methods based on sparse coding. We first …

Low-rank modeling generally refers to a class of methods that solves problems by
representing variables of interest as low-rank matrices. It has achieved great success in …

In this paper, we propose a multi-task correlation particle filter (MCPF) for robust visual
tracking. We first present the multi-task correlation filter (MCF) that takes the …

We propose a multi-expert restoration scheme to address the model drift problem in online
tracking. In the proposed scheme, a tracker and its historical snapshots constitute an expert …

The development of a real-time and robust RGB-T tracker is an extremely challenging task
because the tracked object may suffer from shared and specific challenges in RGB and …

In this paper, we study the challenging problem of tracking the trajectory of a moving object
in a video with possibly very complex background. In contrast to most existing trackers which …

Modeling the target appearance is critical in many modern visual tracking algorithms. Many
tracking-by-detection algorithms formulate the probability of target appearance as …

In this paper, we formulate object tracking in a particle filter framework as a structured multi-
task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT) …

Low-rank matrix approximation has been successfully applied to numerous vision problems
in recent years. In this paper, we propose a novel low-rank prior for blind image deblurring …

In this paper, we propose a robust object tracking algorithm based on a sparse collaborative
model that exploits both holistic templates and local representations to account for drastic …