Kalman filter (KF) based methods for multi-object tracking (MOT) make an assumption that
objects move linearly. While this assumption is acceptable for very short periods of …

Abstract Recently, Multiple Object Tracking has achieved great success, which consists of
object detection, feature embedding, and identity association. Existing methods apply the …

Multi-drone multi-target tracking aims at collabo-ratively detecting and tracking targets
across multiple drones and associating the identities of objects from different drones, which …

For a long time, the most common paradigm in MultiObject Tracking was tracking-by-
detection (TbD), where objects are first detected and then associated over video frames. For …

Advanced driver assistance systems (ADASs) are becoming increasingly common in
modern-day vehicles, as they not only improve safety and reduce accidents but also aid in …

In modern cattle farm management systems, video-based monitoring has become important
in analyzing the high-level behavior of cattle for monitoring their health and predicting …

Representing multimodal behaviors is a critical challenge for pedestrian trajectory
prediction. Previous methods commonly represent this multimodality with multiple latent …

Online multi-object tracking aims at generating the trajectories for multiple objects in the
surveillance scene. It remains a challenging problem in crowded scenes because objects …

Significant progress has been achieved in multi-object tracking (MOT) through the evolution
of detection and re-identification (ReID) techniques. Despite these advancements …

Abstract Multi-Object Tracking (MOT) is an important topic in computer vision. Recent MOT
methods based on the anchor-free paradigm trade complicated hierarchical structures for …