Abstract The Kalman Filter based on uniform assumption has been a crucial motion
estimation module in trackers. However, it has limitations in non-uniform motion modeling …

Multi-object tracking (MOT) is a critical task in various domains, such as traffic analysis,
surveillance, and autonomous vehicles. The joint-detection-and-tracking paradigm has been …

Multi-object tracking (MOT) aims at detecting and assigning identities for objects in videos.
Complicated scenes, severe occlusions, irregular motions, and ambiguous appearances of …

Recent progress in multi-object tracking (MOT) has shown great significance of a robust
scoring mechanism for potential tracks. However, the lack of available data in MOT makes it …

The main challenge of Multiple Object Tracking (MOT) is the efficiency in associating
indefinite number of objects between video frames. Standard motion estimators used in …

In recent years, joint detection and embedding (JDE) has become the research focus in multi-
object tracking (MOT) due to its fast inference speed. JDE models are designed and widely …

Transformers have proven superior performance for a wide variety of tasks since they were
introduced. In recent years, they have drawn attention from the vision community in tasks …

In this paper, we propose a self-supervised learning procedure for training a robust multi-
object tracking (MOT) model given only unlabeled video. While several self-supervisory …

The goal of multi-object tracking (MOT) is detecting and tracking all the objects in a scene,
while keeping a unique identifier for each object. In this paper, we present a new robust …

The popular tracking-by-detection paradigm for multi-object tracking (MOT) focuses on
solving data association problem, of which a robust similarity model lies in the heart. Most …