Minimally invasive surgical instrument visual detection and tracking is one of the core
algorithms of minimally invasive surgical robots. With the development of machine vision …

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 …

Visual tracking, in essence, deals with non-stationary image streams that change over time.
While most existing algorithms are able to track objects well in controlled environments, they …

Abstract Locally Orderless Tracking (LOT) is a visual tracking algorithm that automatically
estimates the amount of local (dis) order in the target. This lets the tracker specialize in both …

Very recently tracking was approached using classification techniques such as support
vector machines. The object to be tracked is discriminated by a classifier from the …

Random Forests (RFs) are frequently used in many computer vision and machine learning
applications. Their popularity is mainly driven by their high computational efficiency during …

A scale invariant feature transform (SIFT) based mean shift algorithm is presented for object
tracking in real scenarios. SIFT features are used to correspond the region of interests …

We pose the following question: what happens when test data not only differs from training
data, but differs from it in a continually evolving way? The classic domain adaptation …

Most existing tracking algorithms construct a representation of a target object prior to the
tracking task starts, and utilize invariant features to handle appearance variation of the target …

Principal component analysis (PCA) has been proven to be an efficient method in pattern
recognition and image analysis. Recently, PCA has been extensively employed for face …