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 …

How to effectively learn temporal variation of target appearance, to exclude the interference
of cluttered background, while maintaining real-time response, is an essential problem of …

With efficient appearance learning models, discriminative correlation filter (DCF) has been
proven to be very successful in recent video object tracking benchmarks and competitions …

Tracking by siamese networks has achieved favorable performance in recent years.
However, most of existing siamese methods do not take full advantage of spatial-temporal …

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 …

Abstract Low Rank Regularization (LRR), in essence, involves introducing a low rank or
approximately low rank assumption to target we aim to learn, which has achieved great …

In this paper, we explore the problem of multiview subspace clustering. We introduce a low-
rank tensor constraint to explore the complementary information from multiple views and …

Visual object tracking is an important area in computer vision, and many tracking algorithms
have been proposed with promising results. Existing object tracking approaches can be …

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 …

Self-representation based subspace learning has shown its effectiveness in many
applications. In this paper, we promote the traditional subspace representation learning by …