Modern multiple object tracking (MOT) systems usually follow the tracking-by-detection
paradigm. It has 1) a detection model for target localization and 2) an appearance …

Multi-object tracking is a fundamental vision problem that has been studied for a long time.
As deep learning brings excellent performances to object detection algorithms, Tracking by …

Many multi-object tracking (MOT) methods follow the framework of" tracking by detection",
which associates the target objects-of-interest based on the detection results. However, due …

Following the tracking-by-detection paradigm, multiple object tracking deals with
challenging scenarios, occlusions or even missing detections; the priority is often given to …

Recent multiobject tracking (MOT) methods usually use very deep neural networks to
achieve competitive accuracy, which inevitably results in degraded inference speed. To …

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) …

Real-time multiple object tracking plays a pivotal role in autonomous driving applications,
particularly in real-world applications. Current methods in various domains often face an …

Multi-object tracking in unmanned aerial vehicle (UAV) videos is an important vision task
and can be applied in a wide range of applications. However, conventional multi-object …

In surveillance and search and rescue applications, it is important to perform multi-target
tracking (MOT) in real-time on low-end devices. Today's MOT solutions employ deep neural …

In this paper, we address various challenges in multi-pedestrian and vehicle tracking in high-
resolution aerial imagery by intensive evaluation of a number of traditional and Deep …