Abstract We present Siam R-CNN, a Siamese re-detection architecture which unleashes the
full power of two-stage object detection approaches for visual object tracking. We combine …

Object class detection, also known as category-level object detection, has become one of
the most focused areas in computer vision in the new century. This article attempts to …

We propose an online visual tracking algorithm by learning discriminative saliency map
using Convolutional Neural Network (CNN). Given a CNN pre-trained on a large-scale …

Adaptive tracking-by-detection methods are widely used in computer vision for tracking
arbitrary objects. Current approaches treat the tracking problem as a classification task and …

Most tracking-by-detection methods employ a local search window around the predicted
object location in the current frame assuming the previous location is accurate, the trajectory …

Robust and efficient target-tracking algorithms embedded on moving platforms, are a
requirement for many computer vision and robotic applications. However, deployment of a …

While determining model complexity is an important problem in machine learning, many
feature learning algorithms rely on cross-validation to choose an optimal number of features …

Ensemble machine learning models have been widely used in hydro-systems modeling as
robust prediction tools that combine multiple decision trees. In this study, three newly …

We propose a randomized ensemble algorithm to model the time-varying appearance of an
object for visual tracking. In contrast with previous online methods for updating classifier …

It is difficult to ensure robustness and accuracy when the traditional morphological method
(TMM) is used to detect weld feature points, especially in an environment with a strong arc …