Object tracking is challenging as target objects often undergo drastic appearance changes
over time. Recently, adaptive correlation filters have been successfully applied to object …

The problem of visual tracking evaluation is sporting a large variety of performance
measures, and largely suffers from lack of consensus about which measures should be used …

In this paper, we address the issue of data imbalance in learning deep models for visual
object tracking. Although it is well known that data distribution plays a crucial role in learning …

We propose an extremely simple but effective regularization technique of convolutional
neural networks (CNNs), referred to as BranchOut, for online ensemble tracking. Our …

In this paper, we present a novel attention-modulated visual tracking algorithm that
decomposes an object into multiple cognitive units, and trains multiple elementary trackers …

Unmanned aerial vehicles (UAVs) are advantageous in their high maneuverability for long-
range outdoor target tracking. In this paper, we develop a UAV-based target tracking and …

The aspect ratio of a target changes frequently during an unmanned aerial vehicle (UAV)
tracking task, which makes the aerial tracking very challenging. Traditional trackers struggle …

In the same vein of discriminative one-shot learning, Siamese networks allow recognizing
an object from a single exemplar with the same class label. However, they do not take …

Current two-stage object detectors, which consists of a region proposal stage and a
refinement stage, may produce unreliable results due to ill-localized proposed regions. To …

Most correlation filter (CF) based trackers utilize the circulant structure of the training data to
learn a linear filter that best regresses this data to a hand-crafted target response. These …