In this paper, we propose a multi-task correlation particle filter (MCPF) for robust visual
tracking. We first present the multi-task correlation filter (MCF) that takes the …

Object tracking is challenging as target objects often undergo drastic appearance changes
over time. Recently, adaptive correlation filters have been successfully applied to object …

Sparse representations have been applied to visual tracking by finding the best candidate
region with minimal reconstruction error based on a set of target templates. However, most …

This work focuses on the problem of multi-label learning with missing labels (MLML), which
aims to label each test instance with multiple class labels given training instances that have …

This study presents a comprehensive review of the automated classification in partial
discharge (PD) source identification and probabilistic interpretation of the classification …

We present an approach to learn a model to estimate the dynamical states at continuous
and discrete inference levels when trajectory information is available. We learn from sparse …

Most existing part-based tracking methods are part-to-part trackers, which usually have two
separated steps including the part matching and target localization. Different from existing …

In this paper, we propose a novel local sparse representation-based tracking framework for
visual tracking. To deeply mine the appearance characteristics of different local patches, the …

Although the correlation filter-based trackers have achieved competitive results both on
accuracy and robustness, the performance of trackers can still be improved because the …

In the most tracking approaches, a score function is utilized to determine which candidate is
the optimal one by measuring the similarity between the candidate and the template …