Twin support vector machines (TWSVM), a novel machine learning algorithm develo**
from traditional support vector machines (SVM), is one of the typical nonparallel support …

Most thermal infrared (TIR) tracking methods are discriminative, treating the tracking
problem as a classification task. However, the objective of the classifier (label prediction) is …

This paper proposes a novel iterative maximum weighted independent set (MWIS) algorithm
for multiple hypothesis tracking (MHT) in a tracking-by-detection framework. MHT converts …

Twin support vector machine is one of the most prominent techniques for classification
problems. It has been applied in various real world applications due to its less computational …

In classification problems, the data samples belonging to different classes have different
number of samples. Sometimes, the imbalance in the number of samples of each class is …

This paper presents a novel visual tracking approach to correlation filter learning toward
peak strength of correlation response. Previous methods leverage all features of the target …

Traditional image recognition technology currently cannot achieve the fast real-time high-
accuracy performance necessary for road recognition in intelligent driving. Deep learning …

Classical tracking algorithms, such as the Bayesian algorithm, extended Kalman filter (EKF),
and classical least-square (CLS) algorithm, have been extensively implemented at target …

This paper proposes a fuzzy-based Lagrangian twin parametric-margin support vector
machine (FLTPMSVM) to reduce the effect of the outliers presented in biomedical data. The …

Correlation filtering based tracking model has received lots of attention and achieved great
success in real-time tracking, however, the lost function in current correlation filtering …