Multi-object tracking (MOT) is an important problem in computer vision which has a wide
range of applications. Formulating MOT as multi-task learning of object detection and re-ID …

Similarity learning has been recognized as a crucial step for object tracking. However,
existing multiple object tracking methods only use sparse ground truth matching as the …

Data association-based multiple object tracking (MOT) involves multiple separated modules
processed or optimized differently, which results in complex method design and requires …

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 …

The recent trend in multiple object tracking (MOT) is heading towards leveraging deep
learning to boost the tracking performance. However, it is not trivial to solve the data …

Modern online multiple object tracking (MOT) methods usually focus on two directions to
improve tracking performance. One is to predict new positions in an incoming frame based …

Multi-object tracking has seen a lot of progress recently, albeit with substantial annotation
costs for developing better and larger labeled datasets. In this work, we remove the need for …

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) …

Abstract Multiple Object Tracking (MOT) has witnessed remarkable advances in recent
years. However, existing studies dominantly request prior knowledge of the tracking target …

Data association across frames is at the core of Multiple Object Tracking (MOT) task. This
problem is usually solved by a traditional graph-based optimization or directly learned via …