Traditional multiple object tracking methods divide the task into two parts: affinity learning
and data association. The separation of the task requires to define a hand-crafted training …

In this article, we detect and track visual objects by using Siamese network or twin neural
network. The Siamese network is constructed to classify moving objects based on the …

Due to balanced accuracy and speed, one-shot models which jointly learn detection and
identification embeddings, have drawn great attention in multi-object tracking (MOT) …

The low reliability of feature and tracking box detection remains a problem in visual object
tracking. Currently, most discriminative correlation filter (DCF) trackers integrate multiple …

When multiple object tracking (MOT) based on the tracking-by-detection paradigm is
implemented, the similarity metric between the current detections and existing tracks plays …

In this paper, we propose a collaborative deep reinforcement learning (C-DRL) method for
multi-object tracking. Most existing multi-object tracking methods employ the tracking-by …

Despite recent progress in Multiple Object Tracking (MOT), several obstacles such as
occlusions, similar objects, and complex scenes remain an open challenge. Meanwhile, a …

Mainstream multi-object tracking methods exploit appearance information and/or motion
information to achieve interframe association. However, dealing with similar appearance …

Over the years, object tracking and detection has emerged as one of the most important
aspects of UAV applications such as surveillance, reconnaissance, etc. In our paper, we …