The goal of this paper is to review the state-of-the-art progress on visual tracking methods,
classify them into different categories, as well as identify future trends. Visual tracking is a …

Visual object tracking is a significant computer vision task which can be applied to many
domains, such as visual surveillance, human computer interaction, and video compression …

Robust object tracking is a challenging task in computer vision. To better solve the partial
occlusion issue, part-based methods are widely used in visual object trackers. However, due …

We propose a novel discriminative learning approach to image set classification by
modeling the image set with its natural second-order statistic, ie covariance matrix. Since …

The online learning methods are popular for visual tracking because of their robust
performance for most video sequences. However, the drifting problem caused by noisy …

Vision tracking is a well-studied framework in vision computing. Develo** a robust visual
tracking system is challenging because of the sudden change in object motion, cluttered …

In this paper, we propose a generative tracking method based on a novel robust linear
regression algorithm. In contrast to existing methods, the proposed Least Soft-thresold …

Covariance matrices have found success in several computer vision applications, including
activity recognition, visual surveillance, and diffusion tensor imaging. This is because they …

Object appearance modeling is crucial for tracking objects, especially in videos captured by
nonstationary cameras and for reasoning about occlusions between multiple moving …

Visual object tracking has become one of the most active research topics in computer vision,
which has been growing in commercial development as well as academic research. Many …