[HTML][HTML] A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation
Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …
belonging to one class is lower than the other. Ensemble learning combines multiple models …
A latent factor analysis-based approach to online sparse streaming feature selection
Online streaming feature selection (OSFS) has attracted extensive attention during the past
decades. Current approaches commonly assume that the feature space of fixed data …
decades. Current approaches commonly assume that the feature space of fixed data …
Multi-cue correlation filters for robust visual tracking
In recent years, many tracking algorithms achieve impressive performance via fusing
multiple types of features, however, most of them fail to fully explore the context among the …
multiple types of features, however, most of them fail to fully explore the context among the …
A survey on online feature selection with streaming features
In the era of big data, the dimensionality of data is increasing dramatically in many domains.
To deal with high dimensionality, online feature selection becomes critical in big data …
To deal with high dimensionality, online feature selection becomes critical in big data …
Visual tracking with convolutional random vector functional link network
L Zhang, PN Suganthan - IEEE transactions on cybernetics, 2016 - ieeexplore.ieee.org
Deep neural network-based methods have recently achieved excellent performance in
visual tracking task. As very few training samples are available in visual tracking task, those …
visual tracking task. As very few training samples are available in visual tracking task, those …
Aerial vehicle tracking by adaptive fusion of hyperspectral likelihood maps
B Uzkent, A Rangnekar… - Proceedings of the IEEE …, 2017 - openaccess.thecvf.com
Hyperspectral cameras provide unique spectral signatures that can be used to solve
surveillance tasks. This paper proposes a novel real-time hyperspectral likelihood maps …
surveillance tasks. This paper proposes a novel real-time hyperspectral likelihood maps …
Robust visual tracking via co-trained kernelized correlation filters
L Zhang, PN Suganthan - Pattern Recognition, 2017 - Elsevier
Recent advances in visual tracking have witnessed the importance of discriminative
classifiers tasked with distinguishing the target from the background. However, a single …
classifiers tasked with distinguishing the target from the background. However, a single …
Visual tracking using strong classifier and structural local sparse descriptors
Sparse coding methods have achieved great success in visual tracking, and we present a
strong classifier and structural local sparse descriptors for robust visual tracking. Since the …
strong classifier and structural local sparse descriptors for robust visual tracking. Since the …
Integrating stereo vision with a CNN tracker for a person-following robot
In this paper, we introduce a stereo vision based CNN tracker for a person following robot.
The tracker is able to track a person in real-time using an online convolutional neural …
The tracker is able to track a person in real-time using an online convolutional neural …
Reliable re-detection for long-term tracking
In long-term object tracking, severe occlusion and deformation could happen to the targets.
Due to the accumulation and propagation of estimation errors, even a few frames of full …
Due to the accumulation and propagation of estimation errors, even a few frames of full …