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 …

Online streaming feature selection (OSFS) has attracted extensive attention during the past
decades. Current approaches commonly assume that the feature space of fixed data …

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 …

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 …

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 …

Hyperspectral cameras provide unique spectral signatures that can be used to solve
surveillance tasks. This paper proposes a novel real-time hyperspectral likelihood maps …

Recent advances in visual tracking have witnessed the importance of discriminative
classifiers tasked with distinguishing the target from the background. However, a single …

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 …

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 …

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 …